Index: trunk/autom4te.cache/requests =================================================================== --- trunk/autom4te.cache/requests (revision 860) +++ trunk/autom4te.cache/requests (revision 861) @@ -1,473 +1,473 @@ # This file was generated by Autom4te 2.71. # It contains the lists of macros which have been traced. # It can be safely removed. @request = ( bless( [ '0', 1, [ '/usr/share/autoconf' ], [ '/usr/share/autoconf/m4sugar/m4sugar.m4', '/usr/share/autoconf/m4sugar/m4sh.m4', '/usr/share/autoconf/autoconf/autoconf.m4', 'aclocal.m4', '/dev/null' ], { - 'AC_FC_PP_DEFINE' => 1, - 'LT_CONFIG_LTDL_DIR' => 1, - 'AC_FC_PP_SRCEXT' => 1, - 'AM_AUTOMAKE_VERSION' => 1, - 'AC_CANONICAL_HOST' => 1, - 'AC_INIT' => 1, - 'AC_CONFIG_LINKS' => 1, - 'AC_CANONICAL_BUILD' => 1, - 'AC_CONFIG_SUBDIRS' => 1, - 'include' => 1, - 'AM_MAKEFILE_INCLUDE' => 1, - '_AM_SUBST_NOTMAKE' => 1, - 'AC_CANONICAL_SYSTEM' => 1, - 'AC_LIBSOURCE' => 1, - 'm4_sinclude' => 1, '_AM_MAKEFILE_INCLUDE' => 1, - 'AC_SUBST' => 1, - 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Volobouev // // March 2009 */ #include #include #include #include #include // The following include is needed for std::sqrt(lapack_double) #include "npstat/nm/lapack_double.h" namespace npstat { /** Base class for a functor that takes no arguments */ template struct Functor0 { typedef Result result_type; inline virtual ~Functor0() {} virtual Result operator()() const = 0; }; /** Copyable reference to Functor0 */ template class Functor0RefHelper : public Functor0 { public: inline Functor0RefHelper(const Functor0& ref) : ref_(ref) {} inline virtual ~Functor0RefHelper() {} inline virtual Result operator()() const {return ref_();} private: const Functor0& ref_; }; /** Convenience function for creating Functor0RefHelper instances */ template inline Functor0RefHelper Functor0Ref(const Functor0& ref) { return Functor0RefHelper(ref); } /** Base class for a functor that takes a single argument */ template struct Functor1 { typedef Result result_type; typedef Arg1 first_argument_type; inline virtual ~Functor1() {} virtual Result operator()(const Arg1&) const = 0; }; /** Copyable reference to Functor1 */ template class Functor1RefHelper : public Functor1 { public: inline Functor1RefHelper(const Functor1& ref) : ref_(ref) {} inline virtual ~Functor1RefHelper() {} inline virtual Result operator()(const Arg1& x1) const {return ref_(x1);} private: const Functor1& ref_; }; /** Convenience function for creating Functor1RefHelper instances */ template inline Functor1RefHelper Functor1Ref(const Functor1& ref) { return Functor1RefHelper(ref); } /** Base class for a functor that takes two arguments */ template struct Functor2 { typedef Result result_type; typedef Arg1 first_argument_type; typedef Arg2 second_argument_type; inline virtual ~Functor2() {} virtual Result operator()(const Arg1&, const Arg2&) const = 0; }; /** Copyable reference to Functor2 */ template class Functor2RefHelper : public Functor2 { public: inline Functor2RefHelper(const Functor2& ref) : ref_(ref) {} inline virtual ~Functor2RefHelper() {} inline virtual Result operator()(const Arg1& x1, const Arg2& x2) const {return ref_(x1, x2);} private: const Functor2& ref_; }; /** Convenience function for creating Functor2RefHelper instances */ template inline Functor2RefHelper Functor2Ref( const Functor2& ref) { return Functor2RefHelper(ref); } /** Base class for a functor that takes three arguments */ template struct Functor3 { typedef Result result_type; typedef Arg1 first_argument_type; typedef Arg2 second_argument_type; typedef Arg3 third_argument_type; inline virtual ~Functor3() {} virtual Result operator()(const Arg1&,const Arg2&,const Arg3&) const=0; }; /** Copyable reference to Functor3 */ template class Functor3RefHelper : public Functor3 { public: inline Functor3RefHelper(const Functor3& ref) : ref_(ref) {} inline virtual ~Functor3RefHelper() {} inline virtual Result operator()(const Arg1& x1, const Arg2& x2, const Arg3& x3) const {return ref_(x1, x2, x3);} private: const Functor3& ref_; }; /** Convenience function for creating Functor3RefHelper instances */ template inline Functor3RefHelper Functor3Ref( const Functor3& ref) { return Functor3RefHelper(ref); } /** Base class for a functor that returns a pair */ template struct PairFunctor { typedef std::pair result_type; typedef Numeric first_argument_type; inline virtual ~PairFunctor() {} virtual std::pair operator()(const Numeric&) const=0; }; /** Copyable reference to PairFunctor */ template class PairFunctorRefHelper : public PairFunctor { public: inline PairFunctorRefHelper(const PairFunctor& ref) : ref_(ref) {} inline virtual ~PairFunctorRefHelper() {} inline virtual std::pair operator()(const Numeric& c) const {return ref_(c);} private: const PairFunctor& ref_; }; /** Convenience function for creating PairFunctorRefHelper instances */ template inline PairFunctorRefHelper PairFunctorRef( const PairFunctor& ref) { return PairFunctorRefHelper(ref); } /** A simple functor which returns a copy of its argument */ template struct Same : public Functor1 { inline virtual ~Same() {} inline Result operator()(const Result& a) const {return a;} inline bool operator==(const Same&) const {return true;} inline bool operator!=(const Same&) const {return false;} }; /** A simple functor which adds a constant to its argument */ template class Shift : public Functor1 { public: inline Shift(const Result& v) : value_(v) {} inline virtual ~Shift() {} inline Result operator()(const Result& a) const {return a + value_;} inline bool operator==(const Shift& r) const {return value_ == r.value_;} inline bool operator!=(const Shift& r) const {return !(*this == r);} private: Shift(); Result value_; }; /** A simple functor which returns a reference to its argument */ template struct SameRef : public Functor1 { inline virtual ~SameRef() {} inline const Result& operator()(const Result& a) const {return a;} inline bool operator==(const SameRef&) const {return true;} inline bool operator!=(const SameRef&) const {return false;} }; /** // Simple functor which ignores is arguments and instead // builds the result using the default constructor of the result type */ template struct DefaultConstructor0 : public Functor0 { inline virtual ~DefaultConstructor0() {} inline Result operator()() const {return Result();} inline bool operator==(const DefaultConstructor0&) const {return true;} inline bool operator!=(const DefaultConstructor0&) const {return false;} }; /** // Simple functor which ignores is arguments and instead // builds the result using the default constructor of the result type */ template struct DefaultConstructor1 : public Functor1 { inline virtual ~DefaultConstructor1() {} inline Result operator()(const Arg1&) const {return Result();} inline bool operator==(const DefaultConstructor1&) const {return true;} inline bool operator!=(const DefaultConstructor1&) const {return false;} }; /** // Simple functor which ignores is arguments and instead // builds the result using the default constructor of the result type */ template struct DefaultConstructor2 : public Functor2 { inline virtual ~DefaultConstructor2() {} inline Result operator()(const Arg1&, const Arg2&) const {return Result();} inline bool operator==(const DefaultConstructor2&) const {return true;} inline bool operator!=(const DefaultConstructor2&) const {return false;} }; /** // Simple functor which ignores is arguments and instead // builds the result using the default constructor of the result type */ template struct DefaultConstructor3 : public Functor3 { inline virtual ~DefaultConstructor3() {} inline Result operator()(const Arg1&, const Arg2&, const Arg3&) const {return Result();} inline bool operator==(const DefaultConstructor3&) const {return true;} inline bool operator!=(const DefaultConstructor3&) const {return false;} }; /** Simple functor which returns a constant */ template class ConstValue0 : public Functor0 { public: inline ConstValue0(const Result& v) : value_(v) {} inline virtual ~ConstValue0() {} inline Result operator()() const {return value_;} inline bool operator==(const ConstValue0& r) const {return value_ == r.value_;} inline bool operator!=(const ConstValue0& r) const {return !(*this == r);} private: ConstValue0(); Result value_; }; /** Simple functor which returns a constant */ template class ConstValue1 : public Functor1 { public: inline ConstValue1(const Result& v) : value_(v) {} inline virtual ~ConstValue1() {} inline Result operator()(const Arg1&) const {return value_;} inline bool operator==(const ConstValue1& r) const {return value_ == r.value_;} inline bool operator!=(const ConstValue1& r) const {return !(*this == r);} private: ConstValue1(); Result value_; }; /** Simple functor which returns a constant */ template class ConstValue2 : public Functor2 { public: inline ConstValue2(const Result& v) : value_(v) {} inline virtual ~ConstValue2() {} inline Result operator()(const Arg1&, const Arg2&) const {return value_;} inline bool operator==(const ConstValue2& r) const {return value_ == r.value_;} inline bool operator!=(const ConstValue2& r) const {return !(*this == r);} private: ConstValue2(); Result value_; }; /** Simple functor which returns a constant */ template class ConstValue3 : public Functor3 { public: inline ConstValue3(const Result& v) : value_(v) {} inline virtual ~ConstValue3() {} inline Result operator()(const Arg1&, const Arg2&, const Arg3&) const {return value_;} inline bool operator==(const ConstValue3& r) const {return value_ == r.value_;} inline bool operator!=(const ConstValue3& r) const {return !(*this == r);} private: ConstValue3(); Result value_; }; /** // Sometimes it becomes necessary to perform an explicit cast for proper // overload resolution of a converting copy constructor */ template struct CastingCopyConstructor : public Functor1 { inline virtual ~CastingCopyConstructor() {} inline Result operator()(const Arg1& a) const {return Result(static_cast(a));} inline bool operator==(const CastingCopyConstructor&) const {return true;} inline bool operator!=(const CastingCopyConstructor&) const {return false;} }; /** // Adaptation for using functors without arguments with simple // cmath-style functions */ template struct FcnFunctor0 : public Functor0 { inline explicit FcnFunctor0(Result (*fcn)()) : fcn_(fcn) {} inline virtual ~FcnFunctor0() {} inline Result operator()() const {return fcn_();} inline bool operator==(const FcnFunctor0& r) const {return fcn_ == r.fcn_;} inline bool operator!=(const FcnFunctor0& r) const {return !(*this == r);} private: FcnFunctor0(); Result (*fcn_)(); }; /** // Adaptation for using single-argument functors with simple // cmath-style functions */ template struct FcnFunctor1 : public Functor1 { inline explicit FcnFunctor1(Result (*fcn)(Arg1)) : fcn_(fcn) {} inline virtual ~FcnFunctor1() {} inline Result operator()(const Arg1& a) const {return fcn_(a);} inline bool operator==(const FcnFunctor1& r) const {return fcn_ == r.fcn_;} inline bool operator!=(const FcnFunctor1& r) const {return !(*this == r);} private: FcnFunctor1(); Result (*fcn_)(Arg1); }; /** // Adaptation for using two-argument functors with simple // cmath-style functions */ template struct FcnFunctor2 : public Functor2 { inline explicit FcnFunctor2(Result (*fcn)(Arg1, Arg2)) : fcn_(fcn) {} inline virtual ~FcnFunctor2() {} inline Result operator()(const Arg1& x, const Arg2& y) const {return fcn_(x, y);} inline bool operator==(const FcnFunctor2& r) const {return fcn_ == r.fcn_;} inline bool operator!=(const FcnFunctor2& r) const {return !(*this == r);} private: FcnFunctor2(); Result (*fcn_)(Arg1, Arg2); }; /** // Adaptation for using three-argument functors with simple // cmath-style functions */ template struct FcnFunctor3 : public Functor3 { inline explicit FcnFunctor3(Result (*fcn)(Arg1,Arg2,Arg3)):fcn_(fcn) {} inline virtual ~FcnFunctor3() {} inline Result operator()(const Arg1&x,const Arg2&y,const Arg3&z) const {return fcn_(x, y, z);} inline bool operator==(const FcnFunctor3& r) const {return fcn_ == r.fcn_;} inline bool operator!=(const FcnFunctor3& r) const {return !(*this == r);} private: FcnFunctor3(); Result (*fcn_)(Arg1, Arg2, Arg3); }; + template < + typename Result1, typename Arg1, + typename Result2, typename Arg2, + class Operator + > + struct ComboFunctor1Helper : public Functor1 + { + inline ComboFunctor1Helper(const Functor1& f1, + const Functor1& f2, + const Operator& op) + : f1_(f1), f2_(f2), op_(op) {} + + inline virtual ~ComboFunctor1Helper() {} + + inline virtual Result1 operator()(const Arg1& x) const + { + return op_(f1_(x), static_cast(f2_(static_cast(x)))); + } + + private: + const Functor1& f1_; + const Functor1& f2_; + const Operator op_; + }; + + /** + // Create a functor which operates on the results produced + // by two other functors. Note that only references to f1 + // and f2 are stored. Lifetimes of f1 and f2 must be longer + // than the lifetime of this functor. The combo functor will + // have the result type and the argument type of f1. + */ + template < + typename Result1, typename Arg1, + typename Result2, typename Arg2, + template class Operator + > + inline ComboFunctor1Helper > + ComboFunctor1(const Functor1& f1, + const Functor1& f2, + const Operator& op) + { + return ComboFunctor1Helper >(f1, f2, op); + } + /** // Functor which extracts a given element from a random access linear // container without bounds checking */ template struct Element1D : public Functor1 { inline explicit Element1D(const unsigned long index) : idx(index) {} inline virtual ~Element1D() {} inline Result operator()(const Container& c) const {return c[idx];} inline bool operator==(const Element1D& r) const {return idx == r.idx;} inline bool operator!=(const Element1D& r) const {return !(*this == r);} private: Element1D(); unsigned long idx; }; /** // Functor which extracts a given element from a random access linear // container with bounds checking */ template struct Element1DAt : public Functor1 { inline explicit Element1DAt(const unsigned long index) : idx(index) {} inline virtual ~Element1DAt() {} inline Result operator()(const Container& c) const {return c.at(idx);} inline bool operator==(const Element1DAt& r) const {return idx == r.idx;} inline bool operator!=(const Element1DAt& r) const {return !(*this == r);} private: Element1DAt(); unsigned long idx; }; /** // Left assignment functor. Works just like normal binary // assignment operator in places where functor is needed. */ template struct assign_left { inline T1& operator()(T1& left, const T2& right) const {return left = right;} }; /** // Right assignment functor. Reverses the assignment direction // in comparison with the normal binary assignment operator. */ template struct assign_right { inline T2& operator()(const T1& left, T2& right) const {return right = left;} }; /** In-place addition on the left side */ template struct pluseq_left { inline T1& operator()(T1& left, const T2& right) const {return left += right;} }; /** In-place addition on the right side */ template struct pluseq_right { inline T2& operator()(const T1& left, T2& right) const {return right += left;} }; /** // In-place addition on the left side preceded by // multiplication of the right argument by a double */ template struct addmul_left { inline explicit addmul_left(const double weight) : w_(weight) {} inline T1& operator()(T1& left, const T2& right) const {return left += w_*right;} private: addmul_left(); double w_; }; /** // In-place addition on the right side preceded by // multiplication of the left argument by a double */ template struct addmul_right { inline explicit addmul_right(const double weight) : w_(weight) {} inline T1& operator()(T1& left, const T2& right) const {return right += w_*left;} private: addmul_right(); double w_; }; /** In-place subtraction on the left side */ template struct minuseq_left { inline T1& operator()(T1& left, const T2& right) const {return left -= right;} }; /** In-place subtraction on the right side */ template struct minuseq_right { inline T2& operator()(const T1& left, T2& right) const {return right -= left;} }; /** In-place multiplication on the left side */ template struct multeq_left { inline T1& operator()(T1& left, const T2& right) const {return left *= right;} }; /** In-place multiplication on the right side */ template struct multeq_right { inline T2& operator()(const T1& left, T2& right) const {return right *= left;} }; /** In-place division on the left side without checking for division by 0 */ template struct diveq_left { inline T1& operator()(T1& left, const T2& right) const {return left /= right;} }; /** In-place division on the right side without checking for division by 0 */ template struct diveq_right { inline T2& operator()(const T1& left, T2& right) const {return right /= left;} }; /** In-place division on the left side. Allow 0/0 = const. */ template struct diveq_left_0by0isC { inline diveq_left_0by0isC() : C(T1()), leftZero(T1()), rightZero(T2()) {} inline explicit diveq_left_0by0isC(const T1& value) : C(value), leftZero(T1()), rightZero(T2()) {} inline T1& operator()(T1& left, const T2& right) const { if (right == rightZero) if (left == leftZero) { left = C; return left; } return left /= right; } private: T1 C; T1 leftZero; T2 rightZero; }; /** In-place division on the right side. Allow 0/0 = const. */ template struct diveq_right_0by0isC { inline diveq_right_0by0isC() : C(T2()), leftZero(T1()), rightZero(T2()) {} inline explicit diveq_right_0by0isC(const T2& value) : C(value), leftZero(T1()), rightZero(T2()) {} inline T2& operator()(const T1& left, T2& right) const { if (left == leftZero) if (right == rightZero) { right = C; return right; } return right /= left; } private: T2 C; T1 leftZero; T2 rightZero; }; /** Left assignment functor preceded by a static cast */ template struct scast_assign_left { inline T1& operator()(T1& left, const T2& right) const {return left = static_cast(right);} }; /** Right assignment functor preceded by a static cast */ template struct scast_assign_right { inline T2& operator()(const T1& left, T2& right) const {return right = static_cast(left);} }; /** In-place addition on the left side preceded by a static cast */ template struct scast_pluseq_left { inline T1& operator()(T1& left, const T2& right) const {return left += static_cast(right);} }; /** In-place addition on the right side preceded by a static cast */ template struct scast_pluseq_right { inline T2& operator()(const T1& left, T2& right) const {return right += static_cast(left);} }; /** In-place subtraction on the left side preceded by a static cast */ template struct scast_minuseq_left { inline T1& operator()(T1& left, const T2& right) const {return left -= static_cast(right);} }; /** In-place subtraction on the right side preceded by a static cast */ template struct scast_minuseq_right { inline T2& operator()(const T1& left, T2& right) const {return right -= static_cast(left);} }; /** Square root functor */ template struct SquareRoot : public Functor1 { inline virtual ~SquareRoot() {} inline virtual T1 operator()(const T1& x) const { static const T1 zero = static_cast(0); if (!(x >= zero)) throw std::domain_error( "In npstat::SquareRoot::operator(): " "argument must be non-negative"); return std::sqrt(x); } }; /** Inverse square root functor */ template struct InverseSquareRoot : public Functor1 { inline virtual ~InverseSquareRoot() {} inline virtual T1 operator()(const T1& x) const { static const T1 zero = static_cast(0); static const T1 one = static_cast(1); if (!(x > zero)) throw std::domain_error( "In npstat::InverseSquareRoot::operator(): " "argument must be positive"); return one/std::sqrt(x); } }; /** // Useful functor for calculating ISE between a function and // a polynomial series */ template class DeltaSquaredSerFcn : public Functor1 { public: // Note that the series coefficients are not copied inline DeltaSquaredSerFcn( const Poly& poly, const Functor& fcn, const double* coeffs, const unsigned maxdeg) : poly_(poly), fcn_(fcn), coeffs_(coeffs), maxdeg_(maxdeg) {} inline virtual ~DeltaSquaredSerFcn() {} inline double operator()(const double& x) const { const double delta = poly_.series(coeffs_, maxdeg_, x) - fcn_(x); return delta*delta; } private: const Poly& poly_; const Functor& fcn_; const double* coeffs_; unsigned maxdeg_; }; /** // Useful functor for calculating powers of another functor or // a cmath-style function. Does not make a copy of the argument // functor, using only a reference. */ template class FunctorPowerFcnHelper : public Functor1 { public: inline FunctorPowerFcnHelper(const Functor& fcn, const double power) : fcn_(fcn), power_(power) {} inline virtual ~FunctorPowerFcnHelper() {} inline double operator()(const double& x) const { if (power_) { const double tmp = fcn_(x); if (power_ == 1.0) return tmp; else if (power_ == 2.0) return tmp*tmp; else if (power_ == -1.0) { assert(tmp); return 1.0/tmp; } else { if (power_ < 0.0) assert(tmp); return std::pow(tmp, power_); } } else return 1.0; } private: const Functor& fcn_; double power_; }; /** Utility function for making FunctorPowerFcnHelper objects */ template inline FunctorPowerFcnHelper FunctorPowerFcn( const Functor& fcn, const double power) { return FunctorPowerFcnHelper(fcn, power); } /** // Useful functor for calculating powers of another functor or // a cmath-style function. Makes a copy of the argument functor. */ template class FunctorPowerFcnCpHelper : public Functor1 { public: inline FunctorPowerFcnCpHelper(const Functor& fcn, const double power) : fcn_(fcn), power_(power) {} inline virtual ~FunctorPowerFcnCpHelper() {} inline double operator()(const double& x) const {return FunctorPowerFcnHelper(fcn_, power_)(x);} private: Functor fcn_; double power_; }; /** Utility function for making FunctorPowerFcnCpHelper objects */ template inline FunctorPowerFcnCpHelper FunctorPowerFcnCp( const Functor& fcn, const double power) { return FunctorPowerFcnCpHelper(fcn, power); } /** Functor for multiplying the result of another functor by a constant */ template class MultiplyByConstHelper : public Functor1 { public: inline MultiplyByConstHelper(const Functor1& fcn, const Result& factor) : fcn_(fcn), factor_(factor) {} inline virtual ~MultiplyByConstHelper() {} inline double operator()(const Arg1& x) const {return factor_*fcn_(x);} private: const Functor1& fcn_; Result factor_; }; /** Utility function for making MultiplyByConstHelper objects */ template inline MultiplyByConstHelper MultiplyByConst( const Functor1& fcn, const Numeric& factor) { return MultiplyByConstHelper(fcn, static_cast(factor)); } } #endif // NPSTAT_SIMPLEFUNCTORS_HH_ Index: trunk/npstat/nm/00README.txt =================================================================== --- trunk/npstat/nm/00README.txt (revision 860) +++ trunk/npstat/nm/00README.txt (revision 861) @@ -1,343 +1,346 @@ The code in this directory should not depend on headers from any other directory of the "npstat" package. This directory contains implementations for a number of useful mathematical objects, integration and root finding codes, etc. The classes and functions can be approximately split into the following subject groups: * Multidimensional arrays, grids, and related utilities * Optimization and root finding * Integration * Interpolation * Orthogonal polynomials and filtering * Searcheable multidimensional structures * Miscellaneous utilities Multidimensional arrays, grids, and related utilities ----------------------------------------------------- ArrayND.hh -- Multidimensional array template. ArrayNDScanner.hh -- A class which can be used to iterate over multidimensional array indices. Useful when the array itself is not available (for example, for iterating over slices, etc). AbsArrayProjector.hh -- Interface class used in iterations over array elements. AbsMultivariateFunctor.hh -- Interface class for a variety of multivariate functor-based calculations. CoordinateSelector.hh -- A trivial AbsMultivariateFunctor implementation which selects one of the elements from the input array. MultivariateFunctorScanner.hh -- Utility for filling array values from multivariate functors. AbsVisitor.hh -- Another interface class used in iterations over array elements. Use this if elements indices is not of interest and only the array value is used. ArrayRange.hh -- Used in constructing array subranges. ArrayShape.hh -- Defines the shape of multidimensional arrays. BoundaryInclusion.hh -- Enumeration of possible boundary inclusions for an interval. BoxND.hh -- Represents rectangles, boxes, and hyperboxes BoxNDScanner.hh -- A class for iterating over all coordinates in a multidimensional box (like using histogram bin centers). EigenMethod.hh -- Helper enum for use with Matrix methods for finding eigenvalues and eigenvectors. SvdMethod.hh -- Helper enum for use with Matrix methods for performing singular value decompositions. GridAxis.hh -- Can be used to define an axis of a rectangular grid (not necessarily equidistant). Interval.hh -- 1-d intervals. Used by BoxND to bulid boxes. Matrix.hh -- A simple matrix template. Outsorces most of its more advanced functionality to LAPACK. matrixIndexPairs.hh -- A utlitity for enumerating matrix elements on the main diagonal and above. PointDimensionality.hh -- Compile-time dimensionality detector for classes like std::array. UniformAxis.hh -- Can be used to define an equidistant axis of a rectangular grid. DualAxis.hh -- Can be used to define either equidistant or non-uniform axis, with O(1) coordinate lookup for equidistant grids. fillArrayCentersPreservingAreas.hh -- It is assumed that we have an array with the number of elements increased by an integer factor in each dimension in comparison with the original array. This array is filled from the original one in the manner appropriate for subsequent smoothing. This is useful in certain rebinning situations, when some quantity calculated on some grid needs to be smoothed and interpolated on a denser grid. truncatedInverseSqrt.hh -- Truncate the square root of a symmetric positive semidefinite matrix by keeping eigenvectors corresponding to largest eigenvalues. Optimization and root finding ----------------------------- MinSearchStatus1D.hh -- Status of minimum search on a 1-d interval. findPeak2D.hh -- Reliable peak finding on 2-d grids. goldenSectionSearch.hh -- Search for 1-d function minimum using the golden section method. findRootInLogSpace.hh -- Search for a solution of a single equation of the type f(x) == rhs where x is some type that can be multiplied by a positive double. Uses interval division. findRootNewtonRaphson.hh -- Search for a solution of a single equation of the type f(x) == rhs using the Newton-Raphson method. findRootUsingBisections.hh -- Search for a solution of a single equation of the type f(x) == rhs using bisections. ScanExtremum1D.hh -- Search for extrema of scanned 1-d curves. See also "MathUtils.hh" header for codes that find roots of quadratic and cubic equations. Integration ----------- AbsIntervalQuadrature1D.hh -- Base class for 1-d quadratures on a closed interval. FejerQuadrature.hh -- 1-d Fejer quadratures (essentially, using Chebyshev polynomials). Gauss1DQuadrature.hh -- 1-d Gauss-Hermite quadrature utilizing "probabilists'" Hermite polynomials with user-defined location and scale. GaussHermiteQuadrature.hh -- 1-d Gaussian quadratures using Hermite polynomials. GaussLegendreQuadrature.hh -- 1-d Gaussian quadratures using Legendre polynomials. GaussLegendreQuadratureQ.hh -- 1-d Gaussian quadratures using Legendre polynomials, in quadruple precision. GaussLegendreQuadrature2D.hh -- 2-d tensor-product Gaussian quadratures using Legendre polynomials. rectangleQuadrature.hh -- Gaussian quadratures on rectangles and hyperrectangles using tensor product integration. RectangleQuadrature1D.hh -- Rectangle integration rule in 1-d. SemiInfGaussianQuadrature.hh -- Quadrature on the [0, Inf] interval with weight exp(-x^2/2) Interpolation ------------- bilinearSection.hh -- Finds the contours of the intersection of a bilinear interpolation cell (specified by values at the corners of the unit square) with a given constant level. For use in higher-level mapping and contouring algorithms. interpolate.hh -- Simple interpolating polynomials (linear, quadratic, cubic) on regularly spaced 1-d grids. LinInterpolatedTable1D.hh -- Linearly interpolated table in 1-d. LinInterpolatedTableND.hh -- Interpolated table in multiple dimensions (with multilinear interpolation). LinearMapper1d.hh -- Linear functor in 1-d for use in interpolation and extrapolation. CircularMapper1d.hh -- Linear coordinate mapper for circular topologies. LogMapper1d.hh -- Functor which is linear in log(x). Useful for interpolating functions of strictly positive quantities. ExpMapper1d.hh -- Functor which is linear in log(y). Useful for interpolating functions that are strictly positive. rescanArray.hh -- A utility for filling one array using values of another. The arrays are treated as values of histogram bins inside the unit box, and interpolations are performed as necessary. Orthogonal polynomials and filtering ------------------------------------ AbsClassicalOrthoPoly1D.hh -- Base class for classical orthogonal polynomials. ClassicalOrthoPoly1DFromWeight.hh -- Orthogonal polynomials on an interval with an arbitrary user-provided weght function. ClassicalOrthoPolys1D.hh -- Concrete implementations of various classical orthogonal polynomials (Legendre, Jacobi, etc). ContOrthoPoly1D.hh -- Continuous orthogonal polynomials for discrete measures (intended for constructing empirical chaos polynomials). ConvolutionEngine1D.hh -- FFTW double precision interface to be used for implementing convolutions in 1-d. ConvolutionEngineND.hh -- FFTW double precision interface to be used for implementing convolutions on grids of arbitrary dimensionality. DiscreteBernsteinPoly1D.hh -- Discrete Bernstein polynomials in one dimension (preserving the partition of unity property). FourierImage.hh -- Wrapper class for memory blocks allocated by fftw_malloc and deallocated by fftw_free. Intended for storing results of Fourier transforms. HeatEq1DNeumannBoundary.hh -- Solution of 1-d heat equation with Neumann boundary conditions (no heat transfer through the boundary). Useful mainly for generating doubly stochastic matrices. +kernelSensitivityMatrix.hh -- Calculation of the sensitivity matrix + for KDE-like density estimation. + OrthoPoly1D.hh -- Discrete orthogonal polynomials in 1-d (typically, for use in linear filters). OrthoPolyND.hh -- Discrete orthogonal polynomials of arbitrary dimensionality in hyperrectangular domains. OrthoPolyMethod.hh -- Enum for the methods used to construct the continuous polynomials (class ContOrthoPoly1D). sineTransformMatrix.hh -- Matrix for the simple (not fast) sine transform on the [0, 1] interval. ScalableClassicalOrthoPoly1D.hh -- Class for scaling and shifting classical orthogonal polynomials. StorablePolySeries1D.hh -- Storable functor for orthogonal polynomial series. See also "MathUtils.hh" header for codes that calculate series for some classical orthogonal polynomial systems. Searcheable multidimensional structures --------------------------------------- CompareByIndex.hh -- Comparison functor for objects that support subsripting. Used by k-d tree code. KDTree.hh -- Balanced k-d tree implementation. All points must be known in advance. Miscellaneous utilities ----------------------- absDifference.hh -- Proper calculation of absolute value and absolute difference of two values for a number of types including unsigned, complex, etc. allocators.hh -- A few utility functions related to memory management. areAllElementsUnique.hh -- A simple template for checking uniqueness of container values using O(N^2) algorithm. binomialCoefficient.hh -- Calculation of binomial coefficients which avoids overflows. closeWithinTolerance.hh -- Closeness comparison for doubles. ComplexComparesAbs.hh -- Use ComplexComparesAbs::less in the templated ComplexComparesFalse.hh code when it makes sense to do comparison of complex numbers by magnitude and comparison of other types by "operator<". ComplexComparesFalse::less returns "false" for complex types and compares other types with "operator<". "more" methods are similar. ConstSubscriptMap.hh -- A variation of std::map template with const subscripting operator. definiteIntegrals.hh -- Definite integrals based on exact expressions. discretizedDistance.hh -- L1 and L2 distances for discretized functions. EquidistantSequence.hh -- Sequences of points equidistant in 1-d linear or log space. fcnOrConst.hh -- Some templated utilities for use in various density estimation codes. GaussianDip.hh -- "Gaussian dip" function. GeneralizedComplex.hh -- Define a type which will be set to std::complex for T from float, double, and long double, and to T for all other types. isMonotonous.hh -- A few simple templates for checking monotonicity of container values. lapack.h -- LAPACK-related declarations (F77 versions). lapack_interface.hh -- High-level interface to some of the LAPACK functions. LongerType.hh -- "Longer" type deduction for binary operations. MathUtils.hh -- Miscellaneous utilities. PairCompare.hh -- Additional comparison functors for std::pair. performSchultzIteration.hh -- Schultz iteration for calculating matrix inverses in long double precision (used internally by the Matrix class). PreciseType.hh -- PreciseType::type is typedefed to "long double" for primitive scalar types and to "std::complex" for complex types. ProperDblFromCmpl.hh -- ProperDblFromCmpl::type is a floating point type which can be multiplied by T when T is complex, otherwise it is just "double". PtrBufferHandle.hh -- Resource handler for an array of pointers to explicitly allocated objects (to complement a vector of pointers). Not for use by application code. SimpleFunctors.hh -- Interface classes and concrete simple functors for a variety of functor-based calculations. SpecialFunctions.hh -- Special functions. sumOfSquares.hh -- Calculate the sum of squares of elements inside a contiguous buffer. timestamp.hh -- Generate a trivial time stamp in the format hh:mm:ss. Triple.hh -- A template with three members, similar to std::pair in purpose and functionality. Index: trunk/npstat/nm/Makefile =================================================================== --- trunk/npstat/nm/Makefile (revision 860) +++ trunk/npstat/nm/Makefile (revision 861) @@ -1,1020 +1,1022 @@ # Makefile.in generated by automake 1.16.5 from Makefile.am. # npstat/nm/Makefile. 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MathUtils.cc OrthoPoly1D.cc GridAxis.cc \ rectangleQuadrature.cc LinInterpolatedTable1D.cc ScanExtremum1D.cc \ bilinearSection.cc ConvolutionEngineND.cc FourierImage.cc SvdMethod.cc \ binomialCoefficient.cc UniformAxis.cc ArrayNDScanner.cc DualAxis.cc \ DiscreteBernsteinPoly1D.cc definiteIntegrals.cc EigenMethod.cc \ goldenSectionSearch.cc timestamp.cc OrthoPolyMethod.cc ContOrthoPoly1D.cc \ lapack_interface.cc AbsClassicalOrthoPoly1D.cc ClassicalOrthoPolys1D.cc \ matrixIndexPairs.cc truncatedInverseSqrt.cc FejerQuadrature.cc \ StorablePolySeries1D.cc SemiInfGaussianQuadrature.cc Gauss1DQuadrature.cc \ HeatEq1DNeumannBoundary.cc performSchultzIteration.cc RecurrenceCoeffs.cc \ GaussLegendreQuadratureQ.cc GaussLegendreQuadrature2D.cc Poly1D.cc \ ScalableClassicalOrthoPoly1D.cc RectangleQuadrature1D.cc findPeak2D.cc \ sineTransformMatrix.cc includedir = ${prefix}/include/npstat/nm include_HEADERS = AbsArrayProjector.hh \ AbsClassicalOrthoPoly1D.hh \ AbsClassicalOrthoPoly1D.icc \ absDifference.hh \ AbsIntervalQuadrature1D.hh \ AbsMultivariateFunctor.hh \ AbsVisitor.hh \ allocators.hh \ areAllElementsUnique.hh \ ArrayND.hh \ ArrayND.icc \ ArrayNDScanner.hh \ ArrayRange.hh \ ArrayShape.hh \ bilinearSection.hh \ binomialCoefficient.hh \ BoundaryInclusion.hh \ BoxND.hh \ BoxND.icc \ BoxNDScanner.hh \ BoxNDScanner.icc \ CircularMapper1d.hh \ ClassicalOrthoPoly1DFromWeight.hh \ ClassicalOrthoPoly1DFromWeight.icc \ ClassicalOrthoPolys1D.hh \ closeWithinTolerance.hh \ CompareByIndex.hh \ ComplexComparesAbs.hh \ ComplexComparesFalse.hh \ ConstSubscriptMap.hh \ ContOrthoPoly1D.hh \ ContOrthoPoly1D.icc \ ConvolutionEngine1D.hh \ ConvolutionEngine1D.icc \ ConvolutionEngineND.hh \ ConvolutionEngineND.icc \ coordAndWeight.hh \ CoordinateSelector.hh \ definiteIntegrals.hh \ DiscreteBernsteinPoly1D.hh \ discretizedDistance.hh \ discretizedDistance.icc \ DualAxis.hh \ EigenMethod.hh \ EquidistantSequence.hh \ ExpMapper1d.hh \ fcnOrConst.hh \ FejerQuadrature.hh \ FejerQuadrature.icc \ fillArrayCentersPreservingAreas.hh \ findPeak2D.hh \ findRootInLogSpace.hh \ findRootInLogSpace.icc \ findRootNewtonRaphson.hh \ findRootNewtonRaphson.icc \ findRootUsingBisections.hh \ findRootUsingBisections.icc \ float128IO.hh \ FourierImage.hh \ Gauss1DQuadrature.hh \ GaussHermiteQuadrature.hh \ GaussHermiteQuadrature.icc \ GaussianDip.hh \ GaussLegendreQuadrature.hh \ GaussLegendreQuadrature.icc \ GaussLegendreQuadrature2D.hh \ GaussLegendreQuadrature2D.icc \ GaussLegendreQuadratureQ.hh \ GaussLegendreQuadratureQ.icc \ GeneralizedComplex.hh \ goldenSectionSearch.hh \ goldenSectionSearch.icc \ GridAxis.hh \ HeatEq1DNeumannBoundary.hh \ interpolate.hh \ interpolate.icc \ Interval.hh \ Interval.icc \ isMonotonous.hh \ KDTree.hh \ KDTree.icc \ + kernelSensitivityMatrix.hh \ + kernelSensitivityMatrix.icc \ lapack.h \ lapack_double.h \ lapack_interface.hh \ lapack_interface.icc \ lapack_interface_double.icc \ lapack_interface_ldouble.icc \ lapack_interface_float.icc \ LinearMapper1d.hh \ LinInterpolatedTable1D.hh \ LinInterpolatedTable1D.icc \ LinInterpolatedTableND.hh \ LinInterpolatedTableND.icc \ LogMapper1d.hh \ LongerType.hh \ MathUtils.hh \ MathUtils.icc \ Matrix.hh \ matrixIndexPairs.hh \ Matrix.icc \ MinSearchStatus1D.hh \ MultivariateFunctorScanner.hh \ OrthoPoly1D.hh \ OrthoPoly1D.icc \ OrthoPolyMethod.hh \ OrthoPolyND.hh \ OrthoPolyND.icc \ PairCompare.hh \ performSchultzIteration.hh \ PointDimensionality.hh \ Poly1D.hh \ polyPrivateUtils.hh \ polyPrivateUtils.icc \ PreciseType.hh \ ProperDblFromCmpl.hh \ PtrBufferHandle.hh \ RectangleQuadrature1D.hh \ RectangleQuadrature1D.icc \ Recurrence.hh \ RecurrenceCoeffs.hh \ RecurrenceCoeffs.icc \ RecurrenceCoeffsTrGauss.hh \ RecurrenceCoeffsTrGauss.icc \ rectangleQuadrature.hh \ rescanArray.hh \ rescanArray.icc \ ScanExtremum1D.hh \ ScalableClassicalOrthoPoly1D.hh \ ScalableClassicalOrthoPoly1D.icc \ SemiInfGaussianQuadrature.hh \ SemiInfGaussianQuadrature.icc \ SimpleFunctors.hh \ sineTransformMatrix.hh \ SpecialFunctions.hh \ std_quadmath.hh \ StorablePolySeries1D.hh \ StorablePolySeries1D.icc \ sumOfSquares.hh \ SvdMethod.hh \ timestamp.hh \ Triple.hh \ truncatedInverseSqrt.hh \ UniformAxis.hh EXTRA_DIST = 00README.txt Index: trunk/npstat/nm/kernelSensitivityMatrix.icc =================================================================== --- trunk/npstat/nm/kernelSensitivityMatrix.icc (revision 0) +++ trunk/npstat/nm/kernelSensitivityMatrix.icc (revision 861) @@ -0,0 +1,170 @@ +#include +#include + +#include "npstat/nm/ClassicalOrthoPolys1D.hh" +#include "npstat/nm/ScalableClassicalOrthoPoly1D.hh" + +#define npstat_kernelSensitivityMatrix_setup_y /**/ \ + const long double ymin = outPoly.xmin(); \ + const long double ymax = outPoly.xmax(); \ + if (ymin >= ymax) throw std::invalid_argument( \ + "In npstat::kernelSensitivityMatrix: please " \ + "provide reasonable ymin and ymax"); \ + const long double ymidpoint = (ymax + ymin)/2.0L; \ + const long double yunit = (ymax - ymin)/2.0L; \ + const unsigned nIntegrationPointsY = yIntegrator.npoints(); \ + std::vector ay(nIntegrationPointsY); \ + std::vector wy(nIntegrationPointsY); \ + yIntegrator.getAllAbscissae(&ay[0], ay.size()); \ + yIntegrator.getAllWeights(&wy[0], wy.size()); \ + const unsigned maxdegP1X = maxdegInputPoly + 1U; \ + Matrix mAcc(nIntegrationPointsY, maxdegP1X, 0); \ + std::vector kValues(nIntegrationPointsY); \ + std::vector polyBuffer(maxdegP1X); \ + long double* const polyValues = &polyBuffer[0]; + + +#define npstat_kernelSensitivityMatrix_normalize /**/ \ + inputPoly.allpoly(x, polyValues, maxdegInputPoly); \ + for (unsigned iy=0; iy 0.0L); \ + } + + +#define npstat_kernelSensitivityMatrix_buildresult /**/ \ + const unsigned maxdegP1Y = maxdegLegendrePoly + 1U; \ + Matrix yPolyMatrix(nIntegrationPointsY, maxdegP1Y); \ + for (unsigned iy=0; iy result(maxdegLegendrePoly, maxdegInputPoly); \ + for (unsigned irow=0; irow +Matrix kernelSensitivityMatrix( + const Fcn2D& kernel, + const InPoly& inputPoly, const unsigned maxdegInputPoly, + const OutPoly& outPoly, const unsigned maxdegLegendrePoly, + const AbsIntervalQuadrature1D& xIntegrator, + const AbsIntervalQuadrature1D& yIntegrator, + const bool normalizeKernel) +{ + if (maxdegInputPoly > inputPoly.maxDegree()) throw std::invalid_argument( + "In npstat::kernelSensitivityMatrix: " + "maximum input poly degree is out of range"); + if (maxdegLegendrePoly > outPoly.maxDegree()) throw std::invalid_argument( + "In npstat::kernelSensitivityMatrix: " + "maximum output poly degree is out of range"); + + const long double xmin = inputPoly.xmin(); + const long double xmax = inputPoly.xmax(); + assert(xmin < xmax); + const long double xmidpoint = (xmax + xmin)/2.0L; + const long double xunit = (xmax - xmin)/2.0L; + + const unsigned nIntegrationPointsX = xIntegrator.npoints(); + std::vector ax(nIntegrationPointsX); + std::vector wx(nIntegrationPointsX); + xIntegrator.getAllAbscissae(&ax[0], ax.size()); + xIntegrator.getAllWeights(&wx[0], wx.size()); + + npstat_kernelSensitivityMatrix_setup_y; + + // Integrate the kernel together with the polys in the x direction + for (unsigned ix=0; ix +Matrix kernelSensitivityMatrix( + const Fcn2D& kernel, + const ContOrthoPoly1D& inputPoly, const unsigned maxdegInputPoly, + const OutPoly& outPoly, const unsigned maxdegLegendrePoly, + const AbsIntervalQuadrature1D& yIntegrator, + const bool normalizeKernel) +{ + if (maxdegInputPoly > inputPoly.maxDegree()) throw std::invalid_argument( + "In npstat::kernelSensitivityMatrix: " + "maximum input poly degree is out of range"); + if (maxdegLegendrePoly > outPoly.maxDegree()) throw std::invalid_argument( + "In npstat::kernelSensitivityMatrix: " + "maximum output poly degree is out of range"); + + const long double xShift = inputPoly.meanCoordinate(); + const unsigned long mSize = inputPoly.measureLength(); + const long double sumOfWeights = inputPoly.sumOfWeights(); + + npstat_kernelSensitivityMatrix_setup_y; + + // Integrate the kernel together with the polys in the x direction + for (unsigned long ix=0; ix + Matrix kernelSensitivityMatrix( + const Fcn2D& kernel, + const InPoly& inputPoly, unsigned maxdegInputPoly, + const OutPoly& outPoly, unsigned maxdegOutPoly, + const AbsIntervalQuadrature1D& xIntegrator, + const AbsIntervalQuadrature1D& yIntegrator, + bool normalizeKernel=true); + + /** + // Template class Fcn2D should provide a method + // "Real operator()(Real x, Real y) const", where "Real" is one of floating + // point types (long double works best). + // + // In the returned matrix, row numbers correspond to the "output" + // polynomial degrees (y space) and column numbers to the "input" + // polynomial degrees (x space). + // + // Set "normalizeKernel" parameter to "false" if the kernel is already known + // to be normalized, that is, Int_ymin^ymax K(x, y) dy = 1 for every x. + */ + template + Matrix kernelSensitivityMatrix( + const Fcn2D& kernel, + const ContOrthoPoly1D& inputPoly, unsigned maxdegInputPoly, + const OutPoly& outPoly, unsigned maxdegOutPoly, + const AbsIntervalQuadrature1D& yIntegrator, + bool normalizeKernel=true); +} + +#include "npstat/nm/kernelSensitivityMatrix.icc" + +#endif // NPSTAT_KERNELSENSITIVITYMATRIX_HH_ Index: trunk/npstat/nm/Makefile.in =================================================================== --- trunk/npstat/nm/Makefile.in (revision 860) +++ trunk/npstat/nm/Makefile.in (revision 861) @@ -1,1020 +1,1022 @@ # Makefile.in generated by automake 1.16.5 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2021 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the 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installcheck installcheck-am \ installdirs maintainer-clean maintainer-clean-generic \ mostlyclean mostlyclean-compile mostlyclean-generic \ mostlyclean-libtool pdf pdf-am ps ps-am python-am python-local \ tags tags-am uninstall uninstall-am uninstall-includeHEADERS .PRECIOUS: Makefile # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: Index: trunk/npstat/stat/kernelSensitivityMatrix.icc =================================================================== --- trunk/npstat/stat/kernelSensitivityMatrix.icc (revision 860) +++ trunk/npstat/stat/kernelSensitivityMatrix.icc (revision 861) @@ -1,159 +0,0 @@ -#include -#include - -#include "npstat/nm/ClassicalOrthoPolys1D.hh" -#include "npstat/nm/ScalableClassicalOrthoPoly1D.hh" - -#define npstat_kernelSensitivityMatrix_setup_y /**/ \ - const long double ymin = i_ymin; \ - const long double ymax = i_ymax; \ - if (ymin >= ymax) throw std::invalid_argument( \ - "In kernelSensitivityMatrix: please " \ - "provide reasonable ymin and ymax"); \ - const long double ymidpoint = (ymax + ymin)/2.0L; \ - const long double yunit = (ymax - ymin)/2.0L; \ - const unsigned nIntegrationPointsY = yIntegrator.npoints(); \ - std::vector ay(nIntegrationPointsY); \ - std::vector wy(nIntegrationPointsY); \ - yIntegrator.getAllAbscissae(&ay[0], ay.size()); \ - yIntegrator.getAllWeights(&wy[0], wy.size()); \ - const unsigned maxdegP1X = maxdegInputPoly + 1U; \ - Matrix mAcc(nIntegrationPointsY, maxdegP1X, 0); \ - std::vector kValues(nIntegrationPointsY); \ - std::vector polyBuffer(maxdegP1X); \ - long double* const polyValues = &polyBuffer[0]; - - -#define npstat_kernelSensitivityMatrix_normalize /**/ \ - inputPoly.allpoly(x, polyValues, maxdegInputPoly); \ - for (unsigned iy=0; iy 0.0L); \ - } - - -#define npstat_kernelSensitivityMatrix_buildresult /**/ \ - const ScalableClassicalOrthoPoly1D legendrePoly( \ - ShiftedLegendreOrthoPoly1D(), ymin, ymax-ymin); \ - const unsigned maxdegP1Y = maxdegLegendrePoly + 1U; \ - Matrix yPolyMatrix(nIntegrationPointsY, maxdegP1Y); \ - for (unsigned iy=0; iy result(maxdegLegendrePoly, maxdegInputPoly); \ - for (unsigned irow=0; irow -Matrix kernelSensitivityMatrix( - const Fcn2D& kernel, const InPoly& inputPoly, - const unsigned maxdegInputPoly, const unsigned maxdegLegendrePoly, - const AbsIntervalQuadrature1D& xIntegrator, - const AbsIntervalQuadrature1D& yIntegrator, - const double i_ymin, const double i_ymax, const bool normalizeKernel) -{ - const long double xmin = inputPoly.xmin(); - const long double xmax = inputPoly.xmax(); - assert(xmin < xmax); - const long double xmidpoint = (xmax + xmin)/2.0L; - const long double xunit = (xmax - xmin)/2.0L; - - const unsigned nIntegrationPointsX = xIntegrator.npoints(); - std::vector ax(nIntegrationPointsX); - std::vector wx(nIntegrationPointsX); - xIntegrator.getAllAbscissae(&ax[0], ax.size()); - xIntegrator.getAllWeights(&wx[0], wx.size()); - - npstat_kernelSensitivityMatrix_setup_y; - - // Integrate the kernel together with the polys in the x direction - for (unsigned ix=0; ix -Matrix kernelSensitivityMatrix( - const Fcn2D& kernel, const ContOrthoPoly1D& inputPoly, - const unsigned maxdegInputPoly, const unsigned maxdegLegendrePoly, - const AbsIntervalQuadrature1D& yIntegrator, - const double i_ymin, const double i_ymax, const bool normalizeKernel) -{ - if (maxdegInputPoly > inputPoly.maxDegree()) throw std::invalid_argument( - "In kernelSensitivityMatrix: maximum input poly degree is out of range"); - - const long double xShift = inputPoly.meanCoordinate(); - const unsigned long mSize = inputPoly.measureLength(); - const long double sumOfWeights = inputPoly.sumOfWeights(); - - npstat_kernelSensitivityMatrix_setup_y; - - // Integrate the kernel together with the polys in the x direction - for (unsigned long ix=0; ix - Matrix kernelSensitivityMatrix( - const Fcn2D& kernel, const InPoly& inputPoly, - unsigned maxdegInputPoly, unsigned maxdegLegendrePoly, - const AbsIntervalQuadrature1D& xIntegrator, - const AbsIntervalQuadrature1D& yIntegrator, - double ymin, double ymax, bool normalizeKernel=true); - - /** - // Template class Fcn2D should provide a method - // "Real operator()(Real x, Real y) const", where "Real" is one of floating - // point types (long double works best). - // - // In the returned matrix, row numbers correspond to the "output" Legendre - // polynomial degrees (y space) and column numbers to the "input" polynomial - // degrees (x space). - // - // Set "normalizeKernel" parameter to "false" if the kernel is already known - // to be normalized, that is, Int_ymin^ymax K(x, y) dy = 1 for every x. - */ - template - Matrix kernelSensitivityMatrix( - const Fcn2D& kernel, const ContOrthoPoly1D& inputPoly, - unsigned maxdegInputPoly, unsigned maxdegLegendrePoly, - const AbsIntervalQuadrature1D& yIntegrator, - double ymin, double ymax, bool normalizeKernel=true); -} - -#include "npstat/stat/kernelSensitivityMatrix.icc" - -#endif // NPSTAT_KERNELSENSITIVITYMATRIX_HH_ Index: trunk/npstat/stat/00README.txt =================================================================== --- trunk/npstat/stat/00README.txt (revision 860) +++ trunk/npstat/stat/00README.txt (revision 861) @@ -1,872 +1,869 @@ The code in this directory can depend on headers from the "nm" and "rng" directories in the "npstat" package. The classes implemented in this directory can be split into several subgroups by their purpose: * Data representation * Descriptive statistics * Statistical distributions * Fitting of parametric models * Statistical testing * Local filtering * Nonparametric density estimation * Deconvolution density estimation (Unfolding) * Nonparametric regression * Interpolation of statistical distributions * Miscellaneous data analysis techniques * Convenience API * Utilities * I/O Data representation ------------------- AbsNtuple.hh -- interface class for ntuples. Implemented in InMemoryNtuple.hh, ArchivedNtuple.hh. To be used by application code. NtHistoFill.hh, NtNtupleFill.hh, NtRectangularCut.hh -- convenience classes and functors for use inside "cycleOverRows", "conditionalCycleOverRows", and other similar ntuple methods. Column.hh -- helper class for AbsNtuple.hh. Can be used to refer to ntuple columns either by name or by column number. Used by AbsNtuple.hh, and should not be used separately. ArchivedNtuple.hh -- class for storing huge ntuples which do not fit in the computer memory. To be used by application code. HistoAxis.hh -- representation of a histogram axis with equidistant bins. NUHistoAxis.hh -- representation of a histogram axis with non-uniform bins. DualHistoAxis.hh -- histogram axis which works reasonably well for both uniform and non-uniform bins. convertAxis.hh -- conversion functions between histogram and grid axes. HistoND.hh -- arbitrary-dimensional histogram template. interpolateHistoND.hh -- interpolation of histogram data to coordinate values between bin centers. mergeTwoHistos.hh -- a utility for smooth blending of two histograms. ProductSymmetricBetaNDCdf.hh -- an interpolation function for blending histograms. InMemoryNtuple.hh -- class for storing small ntuples which completely fit in the computer memory. Works faster than ArchivedNtuple. NtupleBuffer.hh -- data buffer for data exchange between ArchivedNtuple and disk-based archive. OrderedPointND.hh -- a multidimensional point which can be sorted according to multiple sorting criteria. randomHistoFill1D.hh -- utilities for filling 1-d histograms with random samples. randomHistoFillND.hh -- utilities for filling multivariate histograms with random samples. StorableMultivariateFunctor.hh -- Base class for storable multivariate functors. StorableHistoNDFunctor.hh -- Adaptation that represents HistoND as a StorableMultivariateFunctor. HistoNDFunctorInstances.hh -- A number of concrete typedefs for StorableHistoNDFunctor template. StorableInterpolationFunctor.hh -- Adaptation that represents LinInterpolatedTableND as a StorableMultivariateFunctor. InterpolationFunctorInstances.hh -- A number of concrete typedefs for StorableInterpolationFunctor template. Descriptive statistics ---------------------- ArrayProjectors.hh -- helper classes for making lower-dimensional projections of the ArrayND class. Calculate various statistics over projected dimensions (mean, median, etc). arrayStats.hh -- means, standard deviations, and covariance matrices of multivariate arrays. Skewness, kurtosis, and cumulants for 1-d arrays. cumulantConversion.hh -- conversions between central moments and cumulants for 1-d distributions. cumulantUncertainties.hh -- uncertainties for sample cumulants calculated from population cumulants. statUncertainties.hh -- uncertainties of various sample statistics. histoStats.hh -- means and covariance matrices for histograms. kendallsTau.hh -- Kendall's rank correlation coefficient, from a sample of points or from copula. ntupleStats.hh -- means and covariance matrices for ntuples. spearmansRho.hh -- Spearman's rank correlation coefficient, from a sample of points or from copula. logLikelihoodPeak.hh -- summary of a 1-d log-likelihood curve. MultivariateSumAccumulator.hh -- classes in these files are MultivariateSumsqAccumulator.hh intended for calculating MultivariateWeightedSumAccumulator.hh covariance and correlation MultivariateWeightedSumsqAccumulator.hh matrices of multivariate data in a numerically stable manner. These classes can serve as appropriate accumulator functors in various "cycleOverRows" methods of AbsNtuple or as separate tools. SampleAccumulator.hh -- accumulator of items for the purpose of calculating statistical summaries. For use inside histograms, etc. WeightedSampleAccumulator.hh -- similar class for use with weighted items. CircularBuffer.hh -- accumulator of items with fixed length. When this length is exceeded, the oldest items are discarded. Can calculate statistical summaries (mean, stdev) that do not require element sorting. StatAccumulator.hh -- simple, single-pass calculator of mean and standard deviation. Updates a running average, so it works a bit better than a "naive" version. StatAccumulatorPair.hh -- simple, single-pass calculator of mean, standard deviation, and covariance for two variables. StatAccumulatorArr.hh -- simple, single-pass calculator of mean and standard deviation for array sets. CrossCovarianceAccumulator.hh -- single-pass calculator of covariances and correlations for samples with elements represented by arrays. WeightedStatAccumulator.hh -- single-pass calculator of mean and standard deviation for weighted points. WeightedStatAccumulatorPair.hh -- simple, single-pass calculator of mean, standard deviation, and covariance for weighted points. BinSummary.hh -- a five-number summary for SampleAccumulator, StatAccumulator, WeightedSampleAccumulator, and WeightedSampleAccumulator which can be used for making box plots, etc. Allows for direct manipulation of the center value, upper and lower ranges, and min/max value. Statistical distributions ------------------------- AbsDistribution1D.hh -- interface classes for 1-d parametric and tabulated statistical distributions. AbsDiscreteDistribution1D.hh -- interface classes for 1-d discrete statistical distributions. Implemented in DiscreteDistributions1D.hh. AbsDistributionTransform1D.hh -- interface class for 1-d coordinate transforms intended for subsequent use in constructing distributions (in particular, with the TransformedDistribution1D class). AbsCGF1D.hh -- interface class for 1-d cumulant generating functions. AsinhTransform1D.hh -- asinh transform (just like the one used to generate Johnson's S_U curves). SinhAsinhTransform1D.hh -- transform y = sinh(a + b*asinh(x)). LogRatioTransform1D.hh -- log(y/(1-y)) transform with scale and location adjustment (just like the one used to generate Johnson's S_B curves). LogTransform1D.hh -- transform y = log(1 + x). PowerTransform1D.hh -- transform y = a x^p. AbsDistributionND.hh -- interface classes for multivariate statistical distributions. ComparisonDistribution1D.hh -- comparison distributions in one dimension CompositeDistribution1D.hh -- represents univariate statistical distributions whose cumulative distribution functions F(x) can be built by composition of two other cumulative distribution functions: F(x) = G(H(x)). CompositeDistributionND.hh -- represents multivariate statistical distributions decomposed into copula and marginals. Copulas.hh -- distributions which are copulas. CompositeDistros1D.hh -- several implementations of CompositeDistribution1D.hh using concrete distributions. DeltaMixture1D.hh -- a mixture of Dirac delta functions. DensityOrthoPoly1D.hh -- generate a system of orthonormal polynomials for an arbitrary density used as the weight. DistributionMixND.hh -- a mixture of multivariate distributions. Distributions1D.hh -- a number of continuous 1-d statistical distributions. DistributionsND.hh -- a number of continuous multivariate statistical distributions. DiscreteDistributions1D.hh -- several discrete statistical distributions, including Poisson and tabulated. DistributionMix1D.hh -- Mixtures of one-dimensional statistical distributions. distro1DStats.hh -- a utility for empirical calculation of distribution mean, standard deviation, skewness, and kurtosis. EdgeworthSeries1D.hh -- Edgeworth series. EdgeworthSeriesMethod.hh -- Helper class for defining Edgeworth series. EllipticalDistribution.hh -- General implementation of multivariate elliptical ditributions. EllipticalDistributions.hh -- A few concrete multivariate elliptical ditributions with simple parameterizations. ExpTiltedDistribution1D.hh -- Exponentially tilted density (a.k.a. "s-tilted"). FoldedDistribution1D.hh -- 1-d distributions with support folded into an interval. GaussianMixtureEntry.hh -- Helper class for defining Gaussian mixtures. GaussianMixture1D.hh -- One-dimensional Gaussian mixtures. GridRandomizer.hh -- class which knows how to map the unit hypercube into a distribution whose density is represented on an n-d grid (and, therefore, how to generate corresponding random numbers). Not intended for direct use by application code (use class "BinnedDensityND" instead). IdentityTransform1D.hh -- Identity coordinate transformation. JohnsonCurves.hh -- Johnson frequency curves. LeftCensoredDistribution.hh -- left-censored distribution. LocationScaleFamily1D.hh -- Creates a location-scale family from a (typically non-scalable) 1-d distribution. LocationScaleTransform1.hh -- Coordinate transformation of the type y = (x - mu)/sigma, with mu and sigma depending on a single parameter and calculated by two separate functors. ModulatedDistribution1D.hh -- 1-d continuous statistical distributions multiplied by an arbitrary smooth non-negative function. polyExpectation.hh -- expectation values of some polynomial w.r.t. a density function. PolynomialDistro1D.hh -- statistical distribution constructed using orthonormal Legendre polynomial series. PowerHalfCauchy1D.hh -- distribution with unscaled density proportional to x^m/(1 + x^2)^n for x >= 0 (and 0 for x < 0). PowerRatio1D.hh -- distribution with unscaled density proportional to x^m/(1 + x)^n for x >= 0 (and 0 for x < 0). RatioOfNormals.hh -- Distribution generated when one Gaussian variable is divided by another. RightCensoredDistribution.hh -- right-censored distribution. saddlepointDistribution1D.hh -- saddlepoint approximation to distributions. SbMomentsCalculator.hh -- Calculator of moments for S_b curves. Not for use by application code. ScalableGaussND.hh -- multivariate Gaussian with diagonal covariance matrix. SeriesCGF1D.hh -- cumulant generating function implementation with truncated series using known cumulants. TransformedDistribution1D.hh -- 1-d distributions in x but whose density, cdf, etc are specified in the y (transformed) space. TruncatedDistribution1D.hh -- 1-d distributions with truncated support. UGaussConvolution1D.hh -- convolution of uniform and Gaussian distributions. Fitting of parametric models ---------------------------- FitUtils.hh -- fitting of 1-d histograms. See also headers minuitFitJohnsonCurves.hh and MinuitDensityFitFcn1D.hh in the "interfaces" directory. Statistical testing ------------------- AbsBinnedComparison1D.hh -- interface (base class) for comparisons of binned distributions. AbsSmoothGOFTest1D.hh -- interface for one-sample unbinned smooth goodness-of-fit tests for 1-d distributions. AbsTwoDistros1DFunctor.hh -- a base class for functors used to study goodness-of-fit tests for 1-d distributions. AbsUnbinnedGOFTest1D.hh -- interface for one-sample unbinned goodness-of-fit tests for 1-d distributions. BinnedADTest1D.hh -- binned version of the Anderson-Darling test. BinnedKSTest1D.hh -- binned version of the Kolmogorov-Smirnov test. likelihoodStatisticCumulants.hh -- calculating cumulants for various signal testing statistics for subsequent use in Edgeworth serires. LikelihoodStatisticType.hh -- enum for the statistics that can be used to test for signal presence in mixture models or Poisson process models. make_UnbinnedGOFTest1D.hh -- a factory function for creating 1-d GOF test objects. OrthoPolyGOFTest1D.hh -- orthogonal polynomial goodness-of-fit test. PearsonsChiSquared.hh -- chi-squared goodness-of-fit test. scannedKSDistance.hh -- Kolmogorov-Smirnov distance between two (oracle) 1-d distributions. SineGOFTest1D.hh -- Goodness-of-fit test based on the sine expansion of the difference between the empirical cdf and the cdf of the null hypothesis. SmoothCDGOFTest1D.hh -- smooth test for goodness-of-fit that utilizes the empirical comparison density. SmoothGOFTest1D.hh -- smooth test for goodness-of-fit. TwoDistros1DFunctors.hh -- a few functors used to study GoF tests. UnbinnedGOFTests1D.hh -- a variety of goodness-of-fit tests (KS, AD, Cramer-von Mises, Zhang's Z_K, Z_A, and Z_C). UnbinnedGOFTest1DFactory.hh -- a factory for creating a number of GoF tests from a uniform interface. Local filtering --------------- AbsFilter1DBuilder.hh -- interface classes for building local polynomial filters in 1d. Implemented in Filter1DBuilders.hh, BetaFilter1DBuilder.hh, WeightTableFilter1DBuilder.hh, BernsteinFilter1DBuilder.hh, DiscreteGauss1DBuilder.hh, MatrixFilter1DBuilder.hh. Used by LocalPolyFilter1D.hh. AbsPolyFilter1D.hh -- interface class for building univariate smoothers that can be optimized by cross-validation. Implemented in ConstantBandwidthSmoother1D.hh, LocalPolyFilter1D.hh. AbsPolyFilterND.hh -- interface class for building multivariate smoothers that can be optimized by cross-validation. Implemented in LocalPolyFilterND.hh, SequentialPolyFilterND.hh, and KDEFilterND.hh. Used by AbsBandwidthCV.hh, BandwidthCVLeastSquaresND.hh, BandwidthCVPseudoLogliND.hh. AbsSymbetaFilterProvider.hh -- interface class for building local polynomial filters in 1d using kernels from the symmetric beta family. MemoizingSymbetaFilterProvider.hh -- implements AbsSymbetaFilterProvider interface and allows for filter storage and lookup. BernsteinFilter1DBuilder.hh -- concrete class for building filters which smooth densities using Bernstein polynomials. BetaFilter1DBuilder.hh -- concrete class for building filters which smooth densities using beta functions (Bernstein polynomials of non-integer degree). betaKernelsBandwidth.hh -- optimal bandwidth for density estimation with beta kernels. BoundaryHandling.hh -- user API for handling LOrPE boundary methods. BoundaryMethod.hh -- enums for handling LOrPE boundary methods. continuousDegreeTaper.hh -- a method for generating tapers with effectively continuous degree. Intended for use with LocalPolyFilter1D. correctDensityEstimateGHU.hh -- Glad-Hjort-Ushakov correction for density estimates that can be negative DiscreteGauss1DBuilder.hh -- concrete class for building filters which smooth densities using the Green's function of the discretized heat equation. Filter1DBuilders.hh -- concrete classes for building local polynomial filters in 1d. They differ by their treatment of the weight function and boundary effects. MatrixFilter1DBuilder.hh -- building a local polynomial filter from a filtering matrix calculated by other code. WeightTableFilter1DBuilder.hh -- concrete classes for building local polynomial filters in 1d from density scans. KDEFilterND.hh -- KDE filtering (Nadaraya-Watson regression) on a regularly spaced 1-d grid. LocalPolyFilter1D.hh -- local polynomial filtering (regression) on a regularly spaced 1-d grid. LocalPolyFilterND.hh -- local polynomial filtering (regression) on a regularly spaced multivariate grid. LocalMultiFilter1D.hh -- local polynomial filtering with separate polynomials from an orthogonal set. LocalQuadraticLeastSquaresND.hh -- local quadratic polynomial filtering for an irregular set of points (possibly including uncertainties). LOrPE1DCVResult.hh -- an object representing the result of the 1-d LOrPE cross-validation procedure. LOrPE1DFixedDegreeCVPicker.hh -- various algorithms for finding the LOrPE1DFixedDegreeCVRunner.hh optimal LOrPE bandwidth by LOrPE1DFixedDegreeCVScanner.hh cross-validation, for a fixed LOrPE degree. LOrPE1DVariableDegreeCVPicker.hh -- algorithms for finding the optimal LOrPE1DVariableDegreeCVRunner.hh LOrPE bandwidth and degree by cross-validation. lorpeSmooth1D.hh -- high level driver for LocalPolyFilter1D, etc. Intended for density reconstruction from histograms. lorpeBackground1D.hh -- high level driver for fitting mixed models in which signal is parameterized and background is nonparametric. lorpeBackgroundCVDensity1D.hh -- linearization of cross-validation calculations for fitting mixed models with nonparametric background. LOrPEWeightsLocalConvergence.hh -- class that decides whether the iterations of the LOrPE semiparametric mixture Fredholm equation have converged. QuadraticOrthoPolyND.hh -- local quadratic polynomial filtering on a grid. In comparison with LocalPolyFilterND.hh, supports a finer interface to filtering functionality (direct support of an AbsDistributionND as a weight, calculations of gradient and hessian for the fitted surface, fitting is performed on functors rather than ArrayND objects, etc). Used by LocalLogisticRegression.hh. semiMixLocalLogLikelihood.hh -- local likelihood for fitting semiparametric mixture models. SequentialPolyFilterND.hh -- similar to LocalPolyFilterND.hh, but the filtering is performed sequentially for each dimension using 1-d filters. solveForLOrPEWeights.hh -- solver for the non-linear Fredholm equation in the semiparametric mixture model with unbinned LOrPE. SymbetaPolyCollection1D.hh -- class that builds LocalPolyFilter1D objects and memoizes local polynomials for the bandwidth values used. Nonparametric density estimation -------------------------------- AbsBandwidthCV.hh -- interface classes for calculating 1-d and multivariate cross-validation criteria for bandwidth and taper selection. Interfaces declared in this file are implemented in BandwidthCVLeastSquares1D.hh, BandwidthCVLeastSquaresND.hh, BandwidthCVPseudoLogli1D.hh, and BandwidthCVPseudoLogliND.hh. These interfaces are used by classes in CVCopulaSmoother.hh. AbsBandwidthGCV.hh -- interface classes for calculating 1-d and multivariate cross-validation criteria for bandwidth and taper selection. Interfaces declared in this file are implemented in BandwidthGCVLeastSquares1D.hh, BandwidthGCVLeastSquaresND.hh, BandwidthGCVPseudoLogli1D.hh, and BandwidthGCVPseudoLogliND.hh. These interfaces are used by classes in GCVCopulaSmoother.hh. The difference with the series of classes defined in "AbsBandwidthCV.hh" is that the grouping (i.e., the binning) of data is explicitly acknowledged, so that a substantially different set of filters (removing the whole group) can be used for cross-validation. AbsCompositeDistroBuilder.hh -- interface class for building composite distrubutions (which consist of copula and marginals) out of data samples. Implemented in DummyCompositeDistroBuilder.hh and NonparametricCompositeBuilder.hh. AbsDistro1DBuilder.hh -- interface class for building 1-d distributions out of data samples. Implemented in DummyDistro1DBuilder.hh and NonparametricDistro1DBuilder.hh. AbsCopulaSmootherBase.hh -- interface class for building copulas out of data samples. Implemented in AbsCVCopulaSmoother.hh. Used by NonparametricCompositeBuilder.hh. AbsKDE1DKernel.hh -- interface class for simple, brute-force KDE in 1-d without discretization or boundary correction. Implemented in KDE1DHOSymbetaKernel.hh. AbsMarginalSmootherBase.hh -- interface class for building 1-d smoothers of histograms. Implemented in JohnsonKDESmoother.hh, LOrPEMarginalSmoother.hh, ConstantBandwidthSmoother1D.hh, and VariableBandwidthSmoother1D.hh. Used by NonparametricCompositeBuilder.hh. AbsResponseIntervalBuilder.hh -- interface class for making cuts in the inivariate response space when density estimation is performed in the regression context. Implemented in DummyResponseIntervalBuilder.hh and RatioResponseIntervalBuilder.hh. AbsResponseBoxBuilder.hh -- interface class for making cuts in the multivariate response space when density estimation is performed in the regression context. Implemented in DummyResponseBoxBuilder.hh and RatioResponseBoxBuilder.hh. amiseOptimalBandwidth.hh -- function for selecting optimal LOrPE bandwidth values by optimizing AMISE on a reference distribution. Used in JohnsonKDESmoother.cc and ConstantBandwidthSmoother1D.cc. Can also be used by application code. BandwidthCVLeastSquares1D.hh -- class for calculating KDE or LOrPE cross-validation MISE approximations for 1-d density estimates. BandwidthCVLeastSquaresND.hh -- class for calculating KDE or LOrPE cross-validation MISE approximations for multivariate density estimates. BandwidthCVPseudoLogli1D.hh -- class for calculating KDE or LOrPE cross-validation pseudo log likelihood, for 1-d density estimates. BandwidthCVPseudoLogliND.hh -- Class for calculating KDE or LOrPE cross-validation pseudo log likelihood, for multivariate density estimates. buildInterpolatedCompositeDistroND.hh -- Multivariate density estimation in the regression context, with interpolation. buildInterpolatedDistro1DNP.hh -- Univariate density estimation in the regression context, with interpolation. ConstantBandwidthSmoother1D.hh -- 1-d KDE implementation with constant bandwidth. Implements AbsMarginalSmoother interface. ConstantBandwidthSmootherND.hh -- multivariate KDE implementation with constant bandwidth. CVCopulaSmoother.hh -- an interface to copula smoothers which use constant bandwidth LOrPE and select bandwidth by cross-validation. Implemented in LOrPECopulaSmoother.hh, SequentialCopulaSmoother.hh, KDECopulaSmoother.hh, DiscreteGaussCopulaSmoother.hh, and BernsteinCopulaSmoother.hh. Could be used by application code if it needs to develop its own cross-validation method for nonparametric copula estimation. GCVCopulaSmoother.hh -- an interface to copula smoothers working with grouped data and using substantially different filters for cross-validation. Implemented in KDEGroupedCopulaSmoother.hh, LOrPEGroupedCopulaSmoother.hh, and SequentialGroupedCopulaSmoother.hh. empiricalCopula.hh -- functions for building empirical copulas by constructing kd-tree for the data points and then doing lookups in this tree. empiricalCopulaHisto.hh -- function for building empirical copula densities by ordering the data points in multiple dimensions. weightedCopulaHisto.hh -- function for building empirical copula densities by ordering weighted data points in multiple dimensions. HistoNDCdf.hh -- multivariate cumulative distribution function built from a histogram. Its "coveringBox" method can be used to make k-NN type density estimates (and for other purposes). JohnsonKDESmoother.hh -- 1-d KDE implementation with adaptive bandwidth (see comments in the header file for details). Implements AbsMarginalSmoother interface. See also "fitCompositeJohnson.hh" header in the "interfaces" directory for an alternative approach. KDE1D.hh -- Convenience class which aggregates the kernel and the data for brute-force 1-d KDE without boundary correction. KDE1DCV.hh -- Cross-validation utilities for brute-force KDE in 1-d. KDE1DHOSymbetaKernel.hh -- high order Gaussian or symmetric beta kernels for brute-force KDE in 1-d. KDECopulaSmoother.hh -- constant bandwidth multivariate KDE copula smoother in which the bandwidth is selected by cross-validation. Implements CVCopulaSmoother. -kernelSensitivityMatrix.hh -- calculation of the sensitivity matrix - for KDE-like density estimation. - LOrPE1D.hh -- Convenience class which aggregates the kernel and the data for unbinned LOrPE in one dimension. LOrPE1DCV.hh -- Unbinned density estimation by LOrPE with cross-validation. LOrPE1DSymbetaKernel.hh -- high order Gaussian or symmetric beta kernels for unbinned LOrPE in one dimension. LOrPECopulaSmoother.hh -- constant bandwidth multivariate LOrPE copula smoother in which the bandwidth is selected by cross-validation. Implements CVCopulaSmoother. LOrPEMarginalSmoother.hh -- 1-d LOrPE for fitting margins of composite distributions. Basically, interfaces "lorpeSmooth1D" to AbsMarginalSmoother. lorpeMise1D.hh -- Deterministic MISE calculation for reconstructing an arbitrary 1-d density. NonparametricCompositeBuilder.hh -- an implementation of AbsCompositeDistroBuilder. Uses separate density estimation procedures for copula and marginals. orthoPoly1DVProducts.hh -- utility functions for calculating certain statistical properties of 1-d orthogonal polynomials. OSDE1D.hh -- orthogonal series density estimation in one dimension. PolyFilterCollection1D.hh -- collection of LocalPolyFilter1D objects with the same kernel but different bandwidth values. Intended for use with bandwidth scans (for example, in cross-validation scenarios). QuantileTable1D.hh -- density function defined by its quantile table. Can be easily constructed using "empiricalQuantile" function from StatUtils.hh. SequentialCopulaSmoother.hh -- similar to LOrPECopulaSmoother, but the filters are limited to tensor products of univariate filters. variableBandwidthSmooth1D.hh -- KDE with adaptive bandwidth. Used by JohnsonKDESmoother.hh. Deconvolution density estimation (Unfolding) -------------------------------------------- AbsUnfold1D.hh -- interface class for deconvolution density estimation in 1-d (a.k.a. unfolding). AbsUnfoldND.hh -- interface class for multivariate deconvolution density estimation (a.k.a. unfolding). AbsUnfoldingFilterND.hh -- interface class for smoothers used in multivariate unfolding. gaussianResponseMatrix.hh -- helper function for building response matrices for one-dimensional unfolding problems. MultiscaleEMUnfold1D.hh -- a variation of 1-d unfolding algorithm with multiscale filtering and, potentially, faster convergence. productResponseMatrix.hh -- helper function for building sparse response matrices for multivariate unfolding problems. ResponseMatrix.hh -- sparse response matrix representation for multivariate unfolding. SmoothedEMUnfold1D.hh -- expectation-maximization (a.k.a. D'Agostini) unfolding with smoothing for 1-d distributions. SmoothedEMUnfoldND.hh -- expectation-maximization unfolding with smoothing for multivariate distributions. UnfoldingBandwidthScanner1D.hh -- class which gets various information from 1-d unfolding results in a convenient form. UnfoldingBandwidthScannerND.hh -- class which gets various information from multivariate unfolding results in a convenient form. Nonparametric regression ------------------------ LocalLogisticRegression.hh -- facilities for performing local linear and quadratic logistic regression. The interface is designed for use together with Minuit. See also the header "minuitLocalRegression.hh" in the "interfaces" directory. QuantileRegression1D.hh -- nonparametric quantile regression with one predictor. Supports polynomials of arbitrary degrees. Useful for constructing Neyman belts. See also "minuitLocalQuantileRegression1D.hh" header in the "interfaces" directory. LocalQuantileRegression.hh -- multivariate local linear or quadratic quantile regression. Can be used to fit histograms or collections of points. See also "minuitQuantileRegression.hh" header in the "interfaces" directory. CensoredQuantileRegression.hh -- multivariate local linear or quadratic quantile regression which can be used for data samples affected by a one-sided cut. griddedRobustRegression.hh -- framework for local robust regression (in particular, for local least trimmed squares). GriddedRobustRegressionStop.hh -- simple functor for stopping robust regression sequence. AbsLossCalculator.hh -- abstract class for calculating local loss for local robust regression. Implemented in "WeightedLTSLoss.hh" and "TwoPointsLTSLoss.hh". WeightedLTSLoss.hh -- functor for calculating local least trimmed squares with one point removed. TwoPointsLTSLoss.hh -- functor for calculating local least trimmed squares with two points or 1-d stripes removed. Interpolation of statistical distributions ------------------------------------------ AbsGridInterpolatedDistribution.hh -- interface class for interpolating between probability distributions placed at the points of a rectangular parameter grid. Implemented in GridInterpolatedDistribution.hh. To be used by application code. AbsInterpolatedDistribution1D.hh -- interface class for univariate density interpolation algorithms. Implemented by InterpolatedDistribution1D.hh and VerticallyInterpolatedDistribution1D.hh. AbsInterpolationAlgoND.hh -- interface class for multivariate density interpolation algorithms. Implemented by CopulaInterpolationND.hh and UnitMapInterpolationND.hh. Used by GridInterpolatedDistribution.hh. CopulaInterpolationND.hh -- interpolation of distributions represented by CompositeDistributionND. Copulas and quantile functions of the marginals are combined with externally provided weights. UnitMapInterpolationND.hh -- interpolation of distributions mapped to the unit cube by conditional quantile functions. GridInterpolatedDistribution.hh -- class which represents a complete multivariate distribution interpolated in parameters. Constructed incrementally, by adding distributions to the grid points. InterpolatedDistribution1D.hh -- 1-d continuous statistical distribution which interpolates between other distributions by performing linear interpolation of the quantile function. InterpolatedDistro1D1P.hh -- 1-d continuous statistical distribution interpolation on a 1-d parameter grid, with linear interpolation of weights between parameter values. Supports both interpolation of quantile functions and vertical interpolation. InterpolatedDistro1DNP.hh -- 1-d continuous statistical distribution interpolation on a multivariate parameter grid, with multilinear interpolation of weights between parameter values. Supports both interpolation of quantile functions and vertical interpolation. UnitMapInterpolationND.hh -- this class interpolates between multivariate distributions by interpolating between their unit maps. Miscellaneous data analysis techniques -------------------------------------- neymanPearsonWindow1D.hh -- search for likelihood ratio maximum and determination of optimal cuts in 1-d based on likelihood ratio between signal and background. Convenience API --------------- DensityScan1D.hh -- utility class for discretizing 1-d densities. DensityScanND.hh -- functor for filling multidimensional arrays with multivariate density scans. Calculates the density in the bin center. discretizationErrorND.hh -- function for calculating the ISE due to discretization of multivariate densities. DensityAveScanND.hh -- functor for filling multidimensional arrays with multivariate density scans. Integrates the density over the bin area. Distribution1DFactory.hh -- creation of a number of 1-d distributions from a uniform interface. scanSymmetricDensityAsWeight.hh -- determines density support and scans a multivariate density in a manner suitable for subsequent construction of orthogonal polynomial systems. Fills one quadrant (octant, etc) only. scanMultivariateDensityAsWeight.hh -- determines density support and scans a multivariate density in a manner suitable for subsequent construction of orthogonal polynomial systems. Performs a complete scan. Utilities --------- AllSymbetaParams1D.hh -- complete set of parameters for building 1-d filters from the symmetric beta family. buildInterpolatedHelpers.hh -- utilities for nonparametric interpolation of statistical distributions. Not for use by application code. bivariateChiSquare.hh -- calculation of the chi-square in the bivariate case, with the covariance matrix parameterized by the standard deviations and the correlation coefficient. histoUtils.hh -- utilities related to special ways of filling histograms, etc. mirrorWeight.hh -- helper function for scanning multivariate densities. Used by LocalPolyFilterND and KDEFilterND codes. multinomialCovariance1D.hh -- helper function for building multinomial distribution covariance matrices. NMCombinationSequencer.hh -- helper class for a certain type of integer permutations (distinct ways of choosing M out of N objects). sampleDistro1DWithWeight.hh -- special acceptance-rejection sampling from a density. StatUtils.hh -- a few useful functions which did not fit naturally anywhere else. SymbetaParams1D.hh -- collects the parameters of symmetric beta kernels. volumeDensityFromBinnedRadial.hh -- convert a density which was obtained from a histogram of radius values into the density per unit area (or per unit volume or hypervolume). WeightedDistro1DPtr.hh -- associates a pointer to AbsDistribution1D with a weight. Not for use by application code. I/O --- Distribution1DReader.hh -- factory for deserializing 1-d distribution functions. DistributionNDReader.hh -- factory for deserializing N-d distribution functions. distributionReadError.hh -- this code throws an appropriate exception if input I/O operations fail for a distribution previously stored on disk. DiscreteDistribution1DReader.hh -- factory for deserializing 1-d discrete distributions. DistributionTransform1DReader.hh -- factory for deserializing 1-d transforms. fillHistoFromText.hh -- utility for filling histograms from text files similar utility for ntuples in declared in the AbsNtuple.hh header). LocalPolyFilter1DReader.hh -- a reader factory for classes derived from LocalPolyFilter1D. NtupleRecordTypes.hh -- mechanisms for locating parts of the ArchivedNtuple NtupleRecordTypesFwd.hh in the archive. Not for use by application code. NtupleReference.hh -- special reference type for ArchivedNtuple. StorableMultivariateFunctorReader.hh -- factory for deserializing for storable multivariate functors. UnfoldingFilterNDReader.hh -- reader factory for classes derived from AbsUnfoldingFilterND. Index: trunk/npstat/stat/Makefile.am =================================================================== --- trunk/npstat/stat/Makefile.am (revision 860) +++ trunk/npstat/stat/Makefile.am (revision 861) @@ -1,448 +1,446 @@ AM_CPPFLAGS = -I@top_srcdir@/ $(DEPS_CFLAGS) noinst_LTLIBRARIES = libstat.la libstat_la_SOURCES = AbsDistribution1D.cc AbsUnfoldND.cc AbsKDE1DKernel.cc \ AbsDistributionND.cc amiseOptimalBandwidth.cc CompositeDistribution1D.cc \ CompositeDistributionND.cc CopulaInterpolationND.cc SymbetaParams1D.cc \ Distribution1DFactory.cc Distribution1DReader.cc DistributionNDReader.cc \ Distributions1D.cc DistributionsND.cc CrossCovarianceAccumulator.cc \ fitSbParameters.cc StatAccumulatorArr.cc HistoAxis.cc ResponseMatrix.cc \ InterpolatedDistribution1D.cc JohnsonCurves.cc JohnsonKDESmoother.cc \ LocalPolyFilter1D.cc logLikelihoodPeak.cc PolyFilterCollection1D.cc \ SbMomentsBigGamma.cc SbMomentsCalculator.cc gaussianResponseMatrix.cc \ SequentialCopulaSmoother.cc SequentialPolyFilterND.cc StatAccumulator.cc \ UnitMapInterpolationND.cc WeightedStatAccumulator.cc AbsNtuple.cc \ QuadraticOrthoPolyND.cc NMCombinationSequencer.cc Filter1DBuilders.cc \ StatAccumulatorPair.cc GridRandomizer.cc ConstantBandwidthSmoother1D.cc \ GaussianMixture1D.cc HistoNDCdf.cc NUHistoAxis.cc AllSymbetaParams1D.cc \ distributionReadError.cc WeightedStatAccumulatorPair.cc AbsUnfold1D.cc \ ProductSymmetricBetaNDCdf.cc DualHistoAxis.cc multinomialCovariance1D.cc \ StorableMultivariateFunctor.cc StorableMultivariateFunctorReader.cc \ TruncatedDistribution1D.cc neymanPearsonWindow1D.cc AsinhTransform1D.cc \ LOrPEMarginalSmoother.cc LeftCensoredDistribution.cc QuantileTable1D.cc \ RightCensoredDistribution.cc AbsDiscreteDistribution1D.cc convertAxis.cc \ DiscreteDistribution1DReader.cc DiscreteDistributions1D.cc lorpeMise1D.cc \ BernsteinFilter1DBuilder.cc BetaFilter1DBuilder.cc AbsFilter1DBuilder.cc \ continuousDegreeTaper.cc RatioOfNormals.cc AbsCVCopulaSmoother.cc \ DensityScan1D.cc BoundaryHandling.cc SmoothedEMUnfold1D.cc Copulas.cc \ PearsonsChiSquared.cc BinnedKSTest1D.cc MultiscaleEMUnfold1D.cc \ AbsBinnedComparison1D.cc BinnedADTest1D.cc LocalPolyFilter1DReader.cc \ MemoizingSymbetaFilterProvider.cc UGaussConvolution1D.cc BinSummary.cc \ SmoothedEMUnfoldND.cc UnfoldingFilterNDReader.cc AbsUnfoldingFilterND.cc \ UnfoldingBandwidthScannerND.cc DistributionMix1D.cc ScalableGaussND.cc \ InterpolatedDistro1D1P.cc AbsDistributionTransform1D.cc LogTransform1D.cc \ DistributionTransform1DReader.cc TransformedDistribution1D.cc AbsCGF1D.cc \ WeightTableFilter1DBuilder.cc VerticallyInterpolatedDistribution1D.cc \ LocalMultiFilter1D.cc LogRatioTransform1D.cc IdentityTransform1D.cc \ VariableBandwidthSmoother1D.cc AbsMarginalSmootherBase.cc OSDE1D.cc \ buildInterpolatedHelpers.cc GridInterpolatedDistribution.cc StatUtils.cc \ AbsCopulaSmootherBase.cc BernsteinCopulaSmoother.cc distro1DStats.cc \ SequentialGroupedCopulaSmoother.cc UnfoldingBandwidthScanner1D.cc \ InterpolatedDistro1DNP.cc volumeDensityFromBinnedRadial.cc \ statUncertainties.cc LocationScaleFamily1D.cc SinhAsinhTransform1D.cc \ KDE1DHOSymbetaKernel.cc EdgeworthSeriesMethod.cc scannedKSDistance.cc \ EdgeworthSeries1D.cc DeltaMixture1D.cc LikelihoodStatisticType.cc \ likelihoodStatisticCumulants.cc GaussianMixtureEntry.cc SeriesCGF1D.cc \ correctDensityEstimateGHU.cc ComparisonDistribution1D.cc \ DistributionMixND.cc DiscreteGauss1DBuilder.cc DensityOrthoPoly1D.cc \ scanMultivariateDensityAsWeight.cc scanSymmetricDensityAsWeight.cc \ DiscreteGaussCopulaSmoother.cc ExpTiltedDistribution1D.cc \ saddlepointDistribution1D.cc MatrixFilter1DBuilder.cc SineGOFTest1D.cc \ PolynomialDistro1D.cc AbsUnbinnedGOFTest1D.cc OrthoPolyGOFTest1D.cc \ UnbinnedGOFTests1D.cc UnbinnedGOFTest1DFactory.cc SmoothCDGOFTest1D.cc \ LOrPE1DSymbetaKernel.cc FoldedDistribution1D.cc solveForLOrPEWeights.cc \ bivariateChiSquare.cc EllipticalDistribution.cc PowerTransform1D.cc \ PowerHalfCauchy1D.cc PowerRatio1D.cc EllipticalDistributions.cc \ make_UnbinnedGOFTest1D.cc AbsTwoDistros1DFunctor.cc johnsonTransform.cc \ TwoDistros1DFunctors.cc AbsSmoothGOFTest1D.cc LocScaleTransform1D.cc \ LognormalTransform1D.cc JohnsonOrthoPoly1D.cc johnsonIntegral.cc includedir = ${prefix}/include/npstat/stat include_HEADERS = AbsBandwidthCV.hh \ AbsBandwidthGCV.hh \ AbsBinnedComparison1D.hh \ AbsBinnedComparison1D.icc \ AbsCGF1D.hh \ AbsCompositeDistroBuilder.hh \ AbsCompositeDistroBuilder.icc \ AbsCopulaSmootherBase.hh \ AbsCopulaSmootherBase.icc \ AbsCVCopulaSmoother.hh \ AbsDiscreteDistribution1D.hh \ AbsDistribution1D.hh \ AbsDistribution1D.icc \ AbsDistributionND.hh \ AbsDistributionND.icc \ AbsDistributionTransform1D.hh \ AbsDistro1DBuilder.hh \ AbsDistro1DBuilder.icc \ AbsFilter1DBuilder.hh \ AbsInterpolatedDistribution1D.hh \ AbsInterpolationAlgoND.hh \ AbsKDE1DKernel.hh \ AbsKDE1DKernel.icc \ AbsLossCalculator.hh \ AbsMarginalSmootherBase.hh \ AbsMarginalSmootherBase.icc \ AbsNtuple.hh \ AbsNtuple.icc \ AbsPolyFilter1D.hh \ AbsPolyFilterND.hh \ AbsResponseBoxBuilder.hh \ AbsResponseIntervalBuilder.hh \ AbsSmoothGOFTest1D.hh \ AbsSmoothGOFTest1D.icc \ AbsSymbetaFilterProvider.hh \ AbsTwoDistros1DFunctor.hh \ AbsUnbinnedGOFTest1D.hh \ AbsUnfold1D.hh \ AbsUnfoldingFilterND.hh \ AbsUnfoldND.hh \ AllSymbetaParams1D.hh \ amiseOptimalBandwidth.hh \ amiseOptimalBandwidth.icc \ ArchivedNtuple.hh \ ArchivedNtuple.icc \ ArrayProjectors.hh \ ArrayProjectors.icc \ arrayStats.hh \ arrayStats.icc \ AsinhTransform1D.hh \ BandwidthCVLeastSquares1D.hh \ BandwidthCVLeastSquares1D.icc \ BandwidthCVLeastSquaresND.hh \ BandwidthCVLeastSquaresND.icc \ BandwidthCVPseudoLogli1D.hh \ BandwidthCVPseudoLogli1D.icc \ BandwidthCVPseudoLogliND.hh \ BandwidthCVPseudoLogliND.icc \ BandwidthGCVLeastSquares1D.hh \ BandwidthGCVLeastSquares1D.icc \ BandwidthGCVLeastSquaresND.hh \ BandwidthGCVLeastSquaresND.icc \ BandwidthGCVPseudoLogli1D.hh \ BandwidthGCVPseudoLogli1D.icc \ BandwidthGCVPseudoLogliND.hh \ BandwidthGCVPseudoLogliND.icc \ BernsteinCopulaSmoother.hh \ BernsteinFilter1DBuilder.hh \ betaKernelsBandwidth.hh \ betaKernelsBandwidth.icc \ BetaFilter1DBuilder.hh \ BinnedADTest1D.hh \ BinnedKSTest1D.hh \ BinSummary.hh \ BinSummary.icc \ bivariateChiSquare.hh \ BoundaryHandling.hh \ BoundaryMethod.hh \ buildInterpolatedCompositeDistroND.hh \ buildInterpolatedCompositeDistroND.icc \ buildInterpolatedDistro1DNP.hh \ buildInterpolatedDistro1DNP.icc \ buildInterpolatedHelpers.hh \ CensoredQuantileRegression.hh \ CensoredQuantileRegression.icc \ CircularBuffer.hh \ CircularBuffer.icc \ Column.hh \ Column.icc \ ComparisonDistribution1D.hh \ CompositeDistribution1D.hh \ CompositeDistributionND.hh \ CompositeDistributionND.icc \ CompositeDistros1D.hh \ ConstantBandwidthSmoother1D.hh \ ConstantBandwidthSmootherND.hh \ ConstantBandwidthSmootherND.icc \ continuousDegreeTaper.hh \ convertAxis.hh \ CopulaInterpolationND.hh \ Copulas.hh \ correctDensityEstimateGHU.hh \ CrossCovarianceAccumulator.hh \ CrossCovarianceAccumulator.icc \ cumulantConversion.hh \ cumulantConversion.icc \ cumulantUncertainties.hh \ cumulantUncertainties.icc \ CVCopulaSmoother.hh \ CVCopulaSmoother.icc \ DeltaMixture1D.hh \ DeltaMixture1D.icc \ DensityAveScanND.hh \ DensityOrthoPoly1D.hh \ DensityScan1D.hh \ DensityScanND.hh \ DiscreteDistribution1DReader.hh \ DiscreteDistributions1D.hh \ DiscreteGauss1DBuilder.hh \ DiscreteGaussCopulaSmoother.hh \ discretizationErrorND.hh \ Distribution1DFactory.hh \ Distribution1DReader.hh \ DistributionTransform1DReader.hh \ DistributionMix1D.hh \ DistributionMixND.hh \ DistributionNDReader.hh \ Distributions1D.hh \ Distributions1D.icc \ DistributionsND.hh \ DistributionsND.icc \ distributionReadError.hh \ distro1DStats.hh \ DualHistoAxis.hh \ DummyCompositeDistroBuilder.hh \ DummyDistro1DBuilder.hh \ DummyResponseBoxBuilder.hh \ DummyResponseIntervalBuilder.hh \ EdgeworthSeries1D.hh \ EdgeworthSeriesMethod.hh \ EllipticalDistribution.hh \ EllipticalDistributions.hh \ empiricalCopula.hh \ empiricalCopulaHisto.hh \ empiricalCopulaHisto.icc \ empiricalCopula.icc \ ExpTiltedDistribution1D.hh \ fillHistoFromText.hh \ fillHistoFromText.icc \ Filter1DBuilders.hh \ FitUtils.hh \ FitUtils.icc \ FoldedDistribution1D.hh \ GaussianMixtureEntry.hh \ GaussianMixture1D.hh \ gaussianResponseMatrix.hh \ GCVCopulaSmoother.hh \ GCVCopulaSmoother.icc \ griddedRobustRegression.hh \ griddedRobustRegression.icc \ GriddedRobustRegressionStop.hh \ GridInterpolatedDistribution.hh \ GridRandomizer.hh \ HistoAxis.hh \ HistoND.hh \ HistoND.icc \ HistoNDCdf.hh \ HistoNDFunctorInstances.hh \ histoStats.hh \ histoStats.icc \ histoUtils.hh \ histoUtils.icc \ IdentityTransform1D.hh \ InMemoryNtuple.hh \ InMemoryNtuple.icc \ InterpolatedDistribution1D.hh \ InterpolatedDistro1D1P.hh \ InterpolatedDistro1DNP.hh \ interpolateHistoND.hh \ interpolateHistoND.icc \ InterpolationFunctorInstances.hh \ JohnsonCurves.hh \ johnsonIntegral.hh \ JohnsonKDESmoother.hh \ JohnsonOrthoPoly1D.hh \ johnsonTransform.hh \ KDE1D.hh \ KDE1DCV.hh \ KDE1DHOSymbetaKernel.hh \ KDECopulaSmoother.hh \ KDECopulaSmoother.icc \ KDEGroupedCopulaSmoother.hh \ KDEGroupedCopulaSmoother.icc \ KDEFilterND.hh \ KDEFilterND.icc \ kendallsTau.hh \ kendallsTau.icc \ - kernelSensitivityMatrix.hh \ - kernelSensitivityMatrix.icc \ LeftCensoredDistribution.hh \ likelihoodStatisticCumulants.hh \ LikelihoodStatisticType.hh \ LocalLogisticRegression.hh \ LocalLogisticRegression.icc \ LocalMultiFilter1D.hh \ LocalMultiFilter1D.icc \ LocalPolyFilter1D.hh \ LocalPolyFilter1D.icc \ LocalPolyFilter1DReader.hh \ LocalPolyFilterND.hh \ LocalPolyFilterND.icc \ LocalQuadraticLeastSquaresND.hh \ LocalQuadraticLeastSquaresND.icc \ LocalQuantileRegression.hh \ LocalQuantileRegression.icc \ LocationScaleFamily1D.hh \ LocationScaleTransform1.hh \ LocationScaleTransform1.icc \ LocScaleTransform1D.hh \ logLikelihoodPeak.hh \ LognormalTransform1D.hh \ LogRatioTransform1D.hh \ LogTransform1D.hh \ LOrPE1D.hh \ LOrPE1D.icc \ LOrPE1DCV.hh \ LOrPE1DCVResult.hh \ LOrPE1DFixedDegreeCVPicker.hh \ LOrPE1DFixedDegreeCVRunner.hh \ LOrPE1DFixedDegreeCVScanner.hh \ LOrPE1DSymbetaKernel.hh \ LOrPE1DSymbetaKernel.icc \ LOrPE1DVariableDegreeCVRunner.hh \ LOrPE1DVariableDegreeCVPicker.hh \ LOrPECopulaSmoother.hh \ LOrPECopulaSmoother.icc \ LOrPEGroupedCopulaSmoother.hh \ LOrPEGroupedCopulaSmoother.icc \ LOrPEMarginalSmoother.hh \ LOrPEWeightsLocalConvergence.hh \ lorpeBackgroundCVDensity1D.hh \ lorpeBackgroundCVDensity1D.icc \ lorpeBackground1D.hh \ lorpeBackground1D.icc \ lorpeMise1D.hh \ lorpeSmooth1D.hh \ lorpeSmooth1D.icc \ make_UnbinnedGOFTest1D.hh \ MatrixFilter1DBuilder.hh \ MemoizingSymbetaFilterProvider.hh \ mergeTwoHistos.hh \ mergeTwoHistos.icc \ mirrorWeight.hh \ ModulatedDistribution1D.hh \ MultiscaleEMUnfold1D.hh \ multinomialCovariance1D.hh \ MultivariateSumAccumulator.hh \ MultivariateSumsqAccumulator.hh \ MultivariateSumsqAccumulator.icc \ MultivariateWeightedSumAccumulator.hh \ MultivariateWeightedSumsqAccumulator.hh \ MultivariateWeightedSumsqAccumulator.icc \ neymanPearsonWindow1D.hh \ NMCombinationSequencer.hh \ NonparametricCompositeBuilder.hh \ NonparametricCompositeBuilder.icc \ NonparametricDistro1DBuilder.hh \ NonparametricDistro1DBuilder.icc \ NtHistoFill.hh \ NtNtupleFill.hh \ NtRectangularCut.hh \ NtRectangularCut.icc \ NtupleBuffer.hh \ NtupleBuffer.icc \ NtupleRecordTypes.hh \ NtupleRecordTypesFwd.hh \ NtupleReference.hh \ ntupleStats.hh \ NUHistoAxis.hh \ OrderedPointND.hh \ OrderedPointND.icc \ orthoPoly1DVProducts.hh \ orthoPoly1DVProducts.icc \ OrthoPolyGOFTest1D.hh \ OrthoPolyGOFTest1D.icc \ OSDE1D.hh \ OSDE1D.icc \ PearsonsChiSquared.hh \ polyExpectation.hh \ polyExpectation.icc \ PolyFilterCollection1D.hh \ PolynomialDistro1D.hh \ PowerHalfCauchy1D.hh \ PowerRatio1D.hh \ PowerTransform1D.hh \ productResponseMatrix.hh \ productResponseMatrix.icc \ ProductSymmetricBetaNDCdf.hh \ QuadraticOrthoPolyND.hh \ QuadraticOrthoPolyND.icc \ QuantileRegression1D.hh \ QuantileRegression1D.icc \ QuantileTable1D.hh \ randomHistoFill1D.hh \ randomHistoFill1D.icc \ randomHistoFillND.hh \ randomHistoFillND.icc \ RatioOfNormals.hh \ RatioResponseBoxBuilder.hh \ RatioResponseBoxBuilder.icc \ RatioResponseIntervalBuilder.hh \ RatioResponseIntervalBuilder.icc \ ResponseMatrix.hh \ RightCensoredDistribution.hh \ saddlepointDistribution1D.hh \ SampleAccumulator.hh \ SampleAccumulator.icc \ sampleDistro1DWithWeight.hh \ SbMomentsCalculator.hh \ ScalableGaussND.hh \ scannedKSDistance.hh \ scanMultivariateDensityAsWeight.hh \ scanSymmetricDensityAsWeight.hh \ semiMixLocalLogLikelihood.hh \ SequentialCopulaSmoother.hh \ SequentialCopulaSmoother.icc \ SequentialGroupedCopulaSmoother.hh \ SequentialGroupedCopulaSmoother.icc \ SequentialPolyFilterND.hh \ SequentialPolyFilterND.icc \ SeriesCGF1D.hh \ SineGOFTest1D.hh \ SineGOFTest1D.icc \ SinhAsinhTransform1D.hh \ SmoothCDGOFTest1D.hh \ SmoothCDGOFTest1D.icc \ SmoothedEMUnfold1D.hh \ SmoothedEMUnfoldND.hh \ SmoothGOFTest1D.hh \ SmoothGOFTest1D.icc \ solveForLOrPEWeights.hh \ spearmansRho.hh \ spearmansRho.icc \ StatAccumulator.hh \ StatAccumulatorArr.hh \ StatAccumulatorPair.hh \ statUncertainties.hh \ StatUtils.hh \ StatUtils.icc \ StorableHistoNDFunctor.hh \ StorableHistoNDFunctor.icc \ StorableInterpolationFunctor.hh \ StorableInterpolationFunctor.icc \ StorableMultivariateFunctor.hh \ StorableMultivariateFunctorReader.hh \ SymbetaParams1D.hh \ TransformedDistribution1D.hh \ TruncatedDistribution1D.hh \ TwoDistros1DFunctors.hh \ TwoPointsLTSLoss.hh \ TwoPointsLTSLoss.icc \ UGaussConvolution1D.hh \ UnbinnedGOFTests1D.hh \ UnbinnedGOFTests1D.icc \ UnbinnedGOFTest1DFactory.hh \ UnfoldingBandwidthScanner1D.hh \ UnfoldingBandwidthScannerND.hh \ UnfoldingFilterNDReader.hh \ UnitMapInterpolationND.hh \ variableBandwidthSmooth1D.hh \ variableBandwidthSmooth1D.icc \ VariableBandwidthSmoother1D.hh \ VerticallyInterpolatedDistribution1D.hh \ volumeDensityFromBinnedRadial.hh \ weightedCopulaHisto.hh \ weightedCopulaHisto.icc \ WeightedDistro1DPtr.hh \ WeightedLTSLoss.hh \ WeightedLTSLoss.icc \ WeightedSampleAccumulator.hh \ WeightedSampleAccumulator.icc \ WeightedStatAccumulator.hh \ WeightedStatAccumulatorPair.hh \ WeightTableFilter1DBuilder.hh EXTRA_DIST = 00README.txt npstat_doxy.hh Index: trunk/npstat/stat/Distributions1D.cc =================================================================== --- trunk/npstat/stat/Distributions1D.cc (revision 860) +++ trunk/npstat/stat/Distributions1D.cc (revision 861) @@ -1,2896 +1,2906 @@ #include #include #include #include #include "geners/binaryIO.hh" #include "npstat/nm/MathUtils.hh" #include "npstat/nm/SpecialFunctions.hh" #include "npstat/nm/interpolate.hh" #include "npstat/stat/Distributions1D.hh" #include "npstat/stat/StatUtils.hh" #include "npstat/stat/distributionReadError.hh" #define SQR2PI 2.5066282746310005 #define SQRT2 1.41421356237309505 #define SQRPI 1.77245385090551603 #define SQRT2L 1.414213562373095048801689L #define SQRPIL 1.77245385090551602729816748L #define TWOPIL 6.28318530717958647692528676656L static long double inverseErf(const long double fval) { long double x = npstat::inverseGaussCdf((fval + 1.0L)/2.0L)/SQRT2L; for (unsigned i=0; i<2; ++i) { const long double guessed = erfl(x); const long double deri = 2.0L/SQRPIL*expl(-x*x); x += (fval - guessed)/deri; } return x; } static unsigned improved_random(npstat::AbsRandomGenerator& g, long double* generatedRandom) { const long double extra = sqrt(DBL_EPSILON); long double u = 0.0L; unsigned callcount = 0; while (u <= 0.0L || u >= 1.0L) { u = g()*(1.0L + extra) - extra/2.0L; u += (g() - 0.5L)*extra; callcount += 2U; } *generatedRandom = u; return callcount; } static unsigned gauss_random(const double mean, const double sigma, npstat::AbsRandomGenerator& g, double* generatedRandom) { assert(generatedRandom); long double r1 = 0.0L, r2 = 0.0L; const unsigned calls = improved_random(g, &r1) + improved_random(g, &r2); *generatedRandom = mean + sigma*sqrtl(-2.0L*logl(r1))*sinl(TWOPIL*(r2-0.5L)); return calls; } // static unsigned gauss_random(const double mean, const double sigma, // npstat::AbsRandomGenerator& g, // double* generatedRandom) // { // assert(generatedRandom); // long double r1 = 0.0L; // const unsigned count = improved_random(g, &r1); // *generatedRandom = mean + sigma*SQRT2*inverseErf(2.0L*r1 - 1.0L); // return count; // } namespace npstat { bool SymmetricBeta1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, n_); return !os.fail(); } SymmetricBeta1D* SymmetricBeta1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { double n; gs::read_pod(in, &n); if (!in.fail()) return new SymmetricBeta1D(location, scale, n); } distributionReadError(in, classname()); return 0; } bool SymmetricBeta1D::isEqual(const AbsDistribution1D& otherBase) const { const SymmetricBeta1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && n_ == r.n_; } SymmetricBeta1D::SymmetricBeta1D(const double location, const double scale, const double power) : AbsScalableDistribution1D(location, scale), n_(power), intpow_(-1) { norm_ = calculateNorm(); } SymmetricBeta1D::SymmetricBeta1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), n_(params[0]), intpow_(-1) { norm_ = calculateNorm(); } double SymmetricBeta1D::calculateNorm() { static const double normcoeffs[11] = { 0.5, 0.75, 0.9375, 1.09375, 1.23046875, 1.353515625, 1.46630859375, 1.571044921875, 1.6692352294921875, 1.76197052001953125, 1.85006904602050781}; if (n_ <= -1.0) throw std::invalid_argument( "In npstat::SymmetricBeta1D::calculateNorm: " "invalid power parameter"); const int intpow = static_cast(floor(n_)); if (static_cast(intpow) == n_ && intpow >= 0 && intpow <= 10) { intpow_ = intpow; return normcoeffs[intpow]; } else return Gamma(1.5 + n_)/sqrt(M_PI)/Gamma(1.0 + n_); } double SymmetricBeta1D::unscaledDensity(const double x) const { const double oneminusrsq = 1.0 - x*x; if (oneminusrsq < 0.0) return 0.0; else { double fcn; switch (intpow_) { case 0: fcn = 1.0; break; case 1: fcn = oneminusrsq; break; case 2: fcn = oneminusrsq*oneminusrsq; break; case 3: fcn = oneminusrsq*oneminusrsq*oneminusrsq; break; case 4: { const double tmp2 = oneminusrsq*oneminusrsq; fcn = tmp2*tmp2; } break; case 5: { const double tmp2 = oneminusrsq*oneminusrsq; fcn = tmp2*tmp2*oneminusrsq; } break; case 6: { const double tmp2 = oneminusrsq*oneminusrsq; fcn = tmp2*tmp2*tmp2; } break; default: fcn = pow(oneminusrsq, n_); break; } return norm_*fcn; } } double SymmetricBeta1D::unscaledCdf(const double x) const { if (x >= 1.0) return 1.0; else if (x <= -1.0) return 0.0; else { double cdf; if (n_ == 0.0) cdf = (x + 1.0)/2.0; else if (n_ == 1.0) cdf = (x*(3.0 - x*x) + 2.0)/4.0; else if (n_ == 2.0) { const double tmp1 = 1.0 + x; cdf = tmp1*tmp1*tmp1*(8.0 + 3.0*x*(x - 3.0))/16.0; } else if (n_ == 3.0) { const double xsq = x*x; cdf = (16. + x*(35. + xsq*(xsq*(21. - 5.*xsq) - 35.0)))/32.0; } else if (n_ == 4.0) { const double tmp1 = 1.0 + x; const double tmp1sq = tmp1*tmp1; const double tmp2 = 128. + x*(-325. + x*(345. + x*(35.*x - 175.))); cdf = tmp1sq*tmp1sq*tmp1*tmp2/256.0; } + else if (n_ == 5.0) + { + static const int c5[6] = {693, -1155, 1386, -990, 385, -63}; + cdf = 0.5 + x*npstat::polySeriesSum(c5, 5, x*x)/512.0; + } + else if (n_ == 6.0) + { + static const int c6[7] = {3003, -6006, 9009, -8580, 5005, -1638, 231}; + cdf = 0.5 + x*npstat::polySeriesSum(c6, 6, x*x)/2048.0; + } else cdf = incompleteBeta(n_+1.0, n_+1.0, (x + 1.0)/2.0); if (cdf < 0.0) cdf = 0.0; if (cdf > 1.0) cdf = 1.0; return cdf; } } double SymmetricBeta1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::SymmetricBeta1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return -1.0; else if (r1 == 1.0) return 1.0; else { double r; if (n_ == 0.0) r = r1*2.0 - 1.0; else r = 2.0*inverseIncompleteBeta(n_+1.0, n_+1.0, r1) - 1.0; if (r < -1.0) r = -1.0; else if (r > 1.0) r = 1.0; return r; } } bool Beta1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, alpha_); gs::write_pod(os, beta_); return !os.fail(); } Beta1D* Beta1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, a, b; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &a); gs::read_pod(in, &b); if (!in.fail()) return new Beta1D(location, scale, a, b); } distributionReadError(in, classname()); return 0; } bool Beta1D::isEqual(const AbsDistribution1D& otherBase) const { const Beta1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && alpha_ == r.alpha_ && beta_ == r.beta_; } Beta1D::Beta1D(const double location, const double scale, const double pa, const double pb) : AbsScalableDistribution1D(location, scale), alpha_(pa), beta_(pb) { norm_ = calculateNorm(); } Beta1D::Beta1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), alpha_(params[0]), beta_(params[1]) { norm_ = calculateNorm(); } double Beta1D::calculateNorm() const { if (!(alpha_ > 0.0 && beta_ > 0.0)) throw std::invalid_argument( "In npstat::Beta1D::calculateNorm: invalid power parameters"); return Gamma(alpha_ + beta_)/Gamma(alpha_)/Gamma(beta_); } double Beta1D::unscaledDensity(const double x) const { if (x < 0.0 || x > 1.0) return 0.0; else if (alpha_ == 1.0) { if (beta_ == 1.0) return 1.0; else return norm_*pow(1.0-x, beta_-1.0); } else return norm_*pow(x, alpha_-1.0)*pow(1.0-x, beta_-1.0); } double Beta1D::unscaledCdf(const double x) const { if (x >= 1.0) return 1.0; else if (x <= 0.0) return 0.0; else if (alpha_ == 1.0 && beta_ == 1.0) return x; else return incompleteBeta(alpha_, beta_, x); } double Beta1D::unscaledExceedance(const double x) const { return 1.0 - unscaledCdf(x); } double Beta1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Beta1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; else if (r1 == 1.0) return 1.0; else if (alpha_ == 1.0 && beta_ == 1.0) return r1; else return inverseIncompleteBeta(alpha_, beta_, r1); } Gamma1D::Gamma1D(const double location, const double scale, const double a) : AbsScalableDistribution1D(location, scale), alpha_(a) { initialize(); } Gamma1D::Gamma1D(double location, double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), alpha_(params[0]) { initialize(); } void Gamma1D::initialize() { if (!(alpha_ > 0.0)) throw std::invalid_argument( "In npstat::Gamma1D::initialize: invalid power parameter"); norm_ = 1.0/Gamma(alpha_); uplim_ = -log(DBL_MIN); } bool Gamma1D::isEqual(const AbsDistribution1D& otherBase) const { const Gamma1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && alpha_ == r.alpha_; } double Gamma1D::unscaledDensity(const double x) const { if (x >= 0.0 && x <= uplim_) return norm_*pow(x, alpha_-1.0)*exp(-x); else return 0.0; } double Gamma1D::unscaledCdf(const double x) const { if (x <= 0.0) return 0.0; else if (x >= uplim_) return 1.0; else return incompleteGamma(alpha_, x); } double Gamma1D::unscaledExceedance(const double x) const { if (x <= 0.0) return 1.0; else if (x >= uplim_) return 0.0; else return incompleteGammaC(alpha_, x); } double Gamma1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Gamma1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; else if (r1 == 1.0) return uplim_; else return inverseIncompleteGamma(alpha_, r1); } bool Gamma1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, alpha_); return !os.fail(); } Gamma1D* Gamma1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, a; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &a); if (!in.fail()) return new Gamma1D(location, scale, a); } distributionReadError(in, classname()); return 0; } Gauss1D::Gauss1D(const double location, const double scale) : AbsScalableDistribution1D(location, scale), xmin_(inverseGaussCdf(0.0)), xmax_(inverseGaussCdf(1.0)) { } Gauss1D::Gauss1D(const double location, const double scale, const std::vector& /* params */) : AbsScalableDistribution1D(location, scale), xmin_(inverseGaussCdf(0.0)), xmax_(inverseGaussCdf(1.0)) { } double Gauss1D::unscaledDensity(const double x) const { if (x < xmin_ || x > xmax_) return 0.0; else return exp(-x*x/2.0)/SQR2PI; } unsigned Gauss1D::random(AbsRandomGenerator& g, double* generatedRandom) const { return gauss_random(location(), scale(), g, generatedRandom); } bool Gauss1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Gauss1D* Gauss1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Gauss1D(location, scale); } double Gauss1D::unscaledCdf(const double x) const { if (x <= xmin_) return 0.0; if (x >= xmax_) return 1.0; if (x < 0.0) return erfc(-x/SQRT2)/2.0; else return (1.0 + erf(x/SQRT2))/2.0; } double Gauss1D::unscaledExceedance(const double x) const { if (x <= xmin_) return 1.0; if (x >= xmax_) return 0.0; if (x > 0.0) return erfc(x/SQRT2)/2.0; else return (1.0 - erf(x/SQRT2))/2.0; } double Gauss1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Gauss1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); return inverseGaussCdf(r1); } double Uniform1D::unscaledDensity(const double x) const { if (x >= 0.0 && x <= 1.0) return 1.0; else return 0.0; } bool Uniform1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Uniform1D* Uniform1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Uniform1D(location, scale); } double Uniform1D::unscaledCdf(const double x) const { if (x <= 0.0) return 0.0; else if (x >= 1.0) return 1.0; else return x; } double Uniform1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Uniform1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); return r1; } double IsoscelesTriangle1D::unscaledDensity(const double x) const { if (x > -1.0 && x < 1.0) return 1.0 - fabs(x); else return 0.0; } bool IsoscelesTriangle1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } IsoscelesTriangle1D* IsoscelesTriangle1D::read( const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new IsoscelesTriangle1D(location, scale); } double IsoscelesTriangle1D::unscaledCdf(const double x) const { if (x <= -1.0) return 0.0; else if (x >= 1.0) return 1.0; else if (x <= 0.0) { const double tmp = 1.0 + x; return 0.5*tmp*tmp; } else { const double tmp = 1.0 - x; return 1.0 - 0.5*tmp*tmp; } } double IsoscelesTriangle1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::IsoscelesTriangle1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return -1.0; else if (r1 == 1.0) return 1.0; else if (r1 <= 0.5) return sqrt(2.0*r1) - 1.0; else return 1.0 - sqrt((1.0 - r1)*2.0); } bool Exponential1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Exponential1D* Exponential1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Exponential1D(location, scale); } double Exponential1D::unscaledDensity(const double x) const { if (x < 0.0) return 0.0; const double eval = exp(-x); return eval < DBL_MIN ? 0.0 : eval; } double Exponential1D::unscaledCdf(const double x) const { return x > 0.0 ? 1.0 - exp(-x) : 0.0; } double Exponential1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Exponential1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 1.0) return -log(DBL_MIN); else return -log(1.0 - r1); } double Exponential1D::unscaledExceedance(const double x) const { if (x < 0.0) return 1.0; const double eval = exp(-x); return eval < DBL_MIN ? 0.0 : eval; } bool Logistic1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Logistic1D* Logistic1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Logistic1D(location, scale); } double Logistic1D::unscaledDensity(const double x) const { const double eval = exp(-x); if (eval < DBL_MIN) return 0.0; else { const double tmp = 1.0 + eval; return eval/tmp/tmp; } } double Logistic1D::unscaledCdf(const double x) const { const double lmax = -log(DBL_MIN); if (x <= -lmax) return 0.0; else if (x >= lmax) return 1.0; else return 1.0/(1.0 + exp(-x)); } double Logistic1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Logistic1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); const double lmax = -log(DBL_MIN); if (r1 == 0.0) return -lmax; else if (r1 == 1.0) return lmax; else return log(r1/(1.0 - r1)); } double Logistic1D::unscaledExceedance(const double x) const { const double lmax = -log(DBL_MIN); if (x >= lmax) return 0.0; else if (x <= -lmax) return 1.0; else { const double eval = exp(-x); return eval/(1.0 + eval); } } bool Quadratic1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, a_); gs::write_pod(os, b_); return !os.fail(); } Quadratic1D* Quadratic1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { double a, b; gs::read_pod(in, &a); gs::read_pod(in, &b); if (!in.fail()) return new Quadratic1D(location, scale, a, b); } distributionReadError(in, classname()); return 0; } bool Quadratic1D::isEqual(const AbsDistribution1D& otherBase) const { const Quadratic1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && a_ == r.a_ && b_ == r.b_; } Quadratic1D::Quadratic1D(const double location, const double scale, const double a, const double b) : AbsScalableDistribution1D(location, scale), a_(a), b_(b) { verifyNonNegative(); } Quadratic1D::Quadratic1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), a_(params[0]), b_(params[1]) { verifyNonNegative(); } void Quadratic1D::verifyNonNegative() { const double a = a_; const double b = b_; if (b == 0.0) { if (fabs(a) > 1.0) throw std::invalid_argument( "In npstat::Quadratic1D::verifyNonNegative:" " invalid distribution parameters"); } else { double x1 = 0.0, x2 = 0.0; const double sixb = 6*b; if (solveQuadratic((2*a-sixb)/sixb, (1-a+b)/sixb, &x1, &x2)) { if (!(fabs(x1 - 0.5) >= 0.5 && fabs(x2 - 0.5) >= 0.5)) throw std::invalid_argument( "In npstat::Quadratic1D::verifyNonNegative:" " invalid distribution parameters"); } } } double Quadratic1D::unscaledDensity(const double x) const { if (x < 0.0 || x > 1.0) return 0.0; else return 1.0 + b_ - a_ + x*(2.0*a_ + 6.0*b_*(x - 1.0)); } double Quadratic1D::unscaledCdf(const double x) const { if (x <= 0.0) return 0.0; else if (x >= 1.0) return 1.0; else return x*(1.0 + b_ - a_ + x*(a_ - 3.0*b_ + 2.0*b_*x)); } double Quadratic1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Quadratic1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; else if (r1 == 1.0) return 1.0; else { const double a = a_; const double b = b_; if (b == 0.0) { if (a == 0.0) return r1; else { double x0 = 0.0, x1 = 0.0; const unsigned n = solveQuadratic( (1.0 - a)/a, -r1/a, &x0, &x1); if (!n) throw std::runtime_error( "In npstat::Quadratic1D::unscaledQuantile: " "no solutions"); if (fabs(x0 - 0.5) < fabs(x1 - 0.5)) return x0; else return x1; } } else { const double twob = 2*b; double x[3] = {0.0}; const unsigned n = solveCubic( (a - 3*b)/twob, (1 - a + b)/twob, -r1/twob, x); if (n == 1U) return x[0]; else { unsigned ibest = 0; double dbest = fabs(x[0] - 0.5); for (unsigned i=1; i()); current.ensureSameId(id); double location, scale, a, b; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &a); gs::read_pod(in, &b); if (!in.fail()) return new LogQuadratic1D(location, scale, a, b); } distributionReadError(in, classname()); return 0; } bool LogQuadratic1D::isEqual(const AbsDistribution1D& otherBase) const { const LogQuadratic1D& r = static_cast( otherBase); return AbsScalableDistribution1D::isEqual(r) && a_ == r.a_ && b_ == r.b_; } LogQuadratic1D::LogQuadratic1D(const double location, const double scale, const double a, const double b) : AbsScalableDistribution1D(location, scale), a_(a), b_(b) { normalize(); } LogQuadratic1D::LogQuadratic1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), a_(params[0]), b_(params[1]) { normalize(); } inline long double LogQuadratic1D::quadInteg(const long double x) const { return dawsonIntegral(x)*expl(x*x); } void LogQuadratic1D::normalize() { ref_ = 0.0L; range_ = 1.0L; k_ = 0.0; s_ = 0.0; norm_ = 1.0; const double b = b_; const double a = a_; if (b > DBL_EPSILON) { k_ = sqrt(6.0*b); s_ = 0.5 - a/(6.0*b); ref_ = quadInteg(k_*s_); range_ = ref_ - quadInteg(k_*(s_ - 1.0)); norm_ = k_/range_; } else if (b < -DBL_EPSILON) { k_ = sqrt(-6.0*b); s_ = 0.5 - a/(6.0*b); ref_ = erfl(k_*s_); range_ = ref_ - erfl(k_*(s_ - 1.0)); norm_ = 2.0*k_/SQRPI/range_; } else if (fabs(a) > DBL_EPSILON) { range_ = expl(2.0L*a) - 1.0L; if (fabs(a) > 1.e-10) norm_ = a/sinh(a); } } double LogQuadratic1D::unscaledDensity(const double x) const { if (x < 0.0 || x > 1.0) return 0.0; const double b = b_; if (fabs(b) > DBL_EPSILON) { const double delta = x - s_; return norm_*exp(6.0*b*delta*delta); } const double a = a_; if (fabs(a) > DBL_EPSILON) return norm_*exp((2.0*x - 1.0)*a); else return 1.0; } double LogQuadratic1D::unscaledCdf(const double x) const { if (x <= 0.0) return 0.0; else if (x >= 1.0) return 1.0; else { const double b = b_; const double a = a_; if (b > DBL_EPSILON) return (ref_ - quadInteg(k_*(s_ - x)))/range_; else if (b < -DBL_EPSILON) return (ref_ - erfl(k_*(s_ - x)))/range_; else if (fabs(a) > DBL_EPSILON) return (expl(2.0L*a*x) - 1.0L)/range_; else return x; } } double LogQuadratic1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::LogQuadratic1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; else if (r1 == 1.0) return 1.0; else { const double b = b_; const double a = a_; double q = 0.0; if (b > DBL_EPSILON) q = s_ - inverseExpsqIntegral(ref_ - r1*range_)/k_; else if (b < -DBL_EPSILON) q = s_ - inverseErf(ref_ - r1*range_)/k_; else if (fabs(a) > DBL_EPSILON) q = logl(r1*range_ + 1.0L)/2.0/a; else q = r1; if (q < 0.0) q = 0.0; else if (q > 1.0) q = 1.0; return q; } } bool TruncatedGauss1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, nsigma_); return !os.fail(); } TruncatedGauss1D* TruncatedGauss1D::read( const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, nsig; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &nsig); if (!in.fail()) return new TruncatedGauss1D(location, scale, nsig); } distributionReadError(in, classname()); return 0; } bool TruncatedGauss1D::isEqual(const AbsDistribution1D& otherBase) const { const TruncatedGauss1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && nsigma_ == r.nsigma_; } void TruncatedGauss1D::initialize() { if (nsigma_ <= 0.0) throw std::invalid_argument( "In npstat::TruncatedGauss1D::initialize: " "invalid truncation parameter"); const double maxSig = inverseGaussCdf(1.0); if (nsigma_ >= maxSig) { nsigma_ = maxSig; cdf0_ = 0.0; norm_ = 1.0; } else { cdf0_ = erfc(nsigma_/SQRT2)/2.0; const double u = (1.0 + erf(nsigma_/SQRT2))/2.0; norm_ = 1.0/(u - cdf0_); } } TruncatedGauss1D::TruncatedGauss1D(const double location, const double scale, const double i_nsigma) : AbsScalableDistribution1D(location, scale), nsigma_(i_nsigma) { initialize(); } TruncatedGauss1D::TruncatedGauss1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), nsigma_(params[0]) { initialize(); } double TruncatedGauss1D::unscaledDensity(const double x) const { if (fabs(x) > nsigma_) return 0.0; else return norm_*exp(-x*x/2.0)/SQR2PI; } unsigned TruncatedGauss1D::random(AbsRandomGenerator& g, double* generatedRandom) const { const double m = location(); const double s = scale(); unsigned cnt = gauss_random(m, s, g, generatedRandom); while (fabs(*generatedRandom - m) > nsigma_*s) cnt += gauss_random(m, s, g, generatedRandom); return cnt; } double TruncatedGauss1D::unscaledCdf(const double x) const { if (x <= -nsigma_) return 0.0; else if (x >= nsigma_) return 1.0; else if (x < 0.0) return (erfc(-x/SQRT2)/2.0 - cdf0_)*norm_; else return ((1.0 + erf(x/SQRT2))/2.0 - cdf0_)*norm_; } double TruncatedGauss1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::TruncatedGauss1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return -nsigma_; else if (r1 == 1.0) return nsigma_; else return inverseGaussCdf(r1/norm_ + cdf0_); } bool MirroredGauss1D::isEqual(const AbsDistribution1D& otherBase) const { const MirroredGauss1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && mu0_ == r.mu0_ && sigma0_ == r.sigma0_; } MirroredGauss1D::MirroredGauss1D(const double location, const double scale, const double mean, double const sigma) : AbsScalableDistribution1D(location, scale), mu0_(mean), sigma0_(sigma) { validate(); } MirroredGauss1D::MirroredGauss1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), mu0_(params[0]), sigma0_(params[1]) { validate(); } bool MirroredGauss1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, mu0_); gs::write_pod(os, sigma0_); return !os.fail(); } MirroredGauss1D* MirroredGauss1D::read( const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, mu, sig; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &mu); gs::read_pod(in, &sig); if (!in.fail()) return new MirroredGauss1D(location, scale, mu, sig); } distributionReadError(in, classname()); return 0; } double MirroredGauss1D::unscaledDensity(const double x) const { if (x < 0.0 || x > 1.0) return 0.0; Gauss1D g(x, sigma0_); long double acc = g.density(mu0_)*1.0L + g.density(-mu0_); for (unsigned k=1; ; ++k) { const long double old = acc; acc += g.density(2.0*k + mu0_); acc += g.density(2.0*k - mu0_); acc += g.density(-2.0*k + mu0_); acc += g.density(-2.0*k - mu0_); if (old == acc) break; } return acc; } long double MirroredGauss1D::ldCdf(const double x) const { if (x <= 0.0) return 0.0L; else if (x >= 1.0) return 1.0L; else { long double acc = 0.0L; { Gauss1D g(mu0_, sigma0_); acc += (g.cdf(x) - g.cdf(0.0)); } { Gauss1D g(-mu0_, sigma0_); acc += (g.cdf(x) - g.cdf(0.0)); } for (unsigned k=1; ; ++k) { const long double old = acc; { Gauss1D g(2.0*k + mu0_, sigma0_); acc += (g.cdf(x) - g.cdf(0.0)); } { Gauss1D g(2.0*k - mu0_, sigma0_); acc += (g.cdf(x) - g.cdf(0.0)); } { Gauss1D g(-2.0*k + mu0_, sigma0_); acc -= (g.exceedance(x) - g.exceedance(0.0)); } { Gauss1D g(-2.0*k - mu0_, sigma0_); acc -= (g.exceedance(x) - g.exceedance(0.0)); } if (old == acc) break; } return acc; } } double MirroredGauss1D::unscaledCdf(const double x) const { return ldCdf(x); } double MirroredGauss1D::unscaledExceedance(const double x) const { return 1.0L - ldCdf(x); } double MirroredGauss1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::MirroredGauss1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; else if (r1 == 1.0) return 1.0; else { const long double ldr1 = r1; double xmin = 0.0; double xmax = 1.0; for (unsigned i=0; i<1000; ++i) { if ((xmax - xmin)/xmax <= 2.0*DBL_EPSILON) break; const double xtry = (xmin + xmax)/2.0; const long double ld = ldCdf(xtry); if (ld == ldr1) return xtry; else if (ld > ldr1) xmax = xtry; else xmin = xtry; } return (xmin + xmax)/2.0; } } void MirroredGauss1D::validate() { if (mu0_ < 0.0 || mu0_ > 1.0) throw std::invalid_argument( "In MirroredGauss1D::validate: interval mean must be within [0, 1]"); if (sigma0_ <= 0.0) throw std::invalid_argument( "In MirroredGauss1D::validate: interval sigma must be positive"); } BifurcatedGauss1D::BifurcatedGauss1D( const double location, const double scale, const double i_leftSigmaFraction, const double i_nSigmasLeft, const double i_nSigmasRight) : AbsScalableDistribution1D(location, scale), leftSigma_(i_leftSigmaFraction*2.0), rightSigma_(2.0 - leftSigma_), nSigmasLeft_(i_nSigmasLeft), nSigmasRight_(i_nSigmasRight) { initialize(); } BifurcatedGauss1D::BifurcatedGauss1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), leftSigma_(params[0]*2.0), rightSigma_(2.0 - leftSigma_), nSigmasLeft_(params[1]), nSigmasRight_(params[2]) { initialize(); } void BifurcatedGauss1D::initialize() { if (leftSigma_ < 0.0 || rightSigma_ < 0.0) throw std::invalid_argument( "In npstat::BifurcatedGauss1D::initialize: " "invalid left sigma fraction"); if (nSigmasLeft_ < 0.0) throw std::invalid_argument( "In npstat::BifurcatedGauss1D::initialize: " "invalid left truncation parameter"); if (nSigmasRight_ < 0.0) throw std::invalid_argument( "In npstat::BifurcatedGauss1D::initialize: " "invalid right truncation parameter"); if (nSigmasLeft_ + nSigmasRight_ == 0.0) throw std::invalid_argument( "In npstat::BifurcatedGauss1D::initialize: " "both truncation parameters can not be 0"); const double maxNSigma = inverseGaussCdf(1.0); if (nSigmasRight_ > maxNSigma) nSigmasRight_ = maxNSigma; if (nSigmasLeft_ > maxNSigma) nSigmasLeft_ = maxNSigma; cdf0Left_ = erfc(nSigmasLeft_/SQRT2)/2.0; cdf0Right_ = (1.0 + erf(nSigmasRight_/SQRT2))/2.0; assert(cdf0Right_ > cdf0Left_); const double leftArea = (0.5 - cdf0Left_)*leftSigma_; const double rightArea = (cdf0Right_ - 0.5)*rightSigma_; norm_ = 1.0/(leftArea + rightArea); leftCdfFrac_ = leftArea/(leftArea + rightArea); } double BifurcatedGauss1D::unscaledDensity(const double x) const { if (x == 0.0) return norm_/SQR2PI; else if (x > 0.0) { if (x > rightSigma_*nSigmasRight_) return 0.0; else { const double dx = x/rightSigma_; return norm_*exp(-dx*dx/2.0)/SQR2PI; } } else { if (x < -leftSigma_*nSigmasLeft_) return 0.0; else { const double dx = x/leftSigma_; return norm_*exp(-dx*dx/2.0)/SQR2PI; } } } double BifurcatedGauss1D::unscaledCdf(const double x) const { if (x == 0.0) return leftCdfFrac_; else if (x > 0.0) { if (x > rightSigma_*nSigmasRight_) return 1.0; else { const double dx = x/rightSigma_; const double cdfDelta = (1.0 + erf(dx/SQRT2))/2.0 - cdf0Right_; return 1.0 + cdfDelta*(1.0 - leftCdfFrac_)/(cdf0Right_ - 0.5); } } else { if (x < -leftSigma_*nSigmasLeft_) return 0.0; else { const double dx = x/leftSigma_; const double cdfDelta = erfc(-dx/SQRT2)/2.0 - cdf0Left_; return cdfDelta*leftCdfFrac_/(0.5 - cdf0Left_); } } } double BifurcatedGauss1D::unscaledExceedance(const double x) const { return 1.0 - unscaledCdf(x); } double BifurcatedGauss1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::BifurcatedGauss1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return -nSigmasLeft_*leftSigma_; else if (r1 == 1.0) return nSigmasRight_*rightSigma_; else { if (r1 == leftCdfFrac_) return 0.0; else if (r1 < leftCdfFrac_) { // Map 0 into cdf0Left_ and leftCdfFrac_ into 0.5 const double arg = r1/leftCdfFrac_*(0.5-cdf0Left_) + cdf0Left_; return leftSigma_*inverseGaussCdf(arg); } else { // Map leftCdfFrac_ into 0.5 and 1.0 into cdf0Right_ const double d = (r1 - leftCdfFrac_)/(1.0 - leftCdfFrac_); const double arg = 0.5 + d*(cdf0Right_ - 0.5); return rightSigma_*inverseGaussCdf(arg); } } } bool BifurcatedGauss1D::isEqual(const AbsDistribution1D& otherBase) const { const BifurcatedGauss1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && leftSigma_ == r.leftSigma_ && rightSigma_ == r.rightSigma_ && nSigmasLeft_ == r.nSigmasLeft_ && nSigmasRight_ == r.nSigmasRight_ && norm_ == r.norm_ && leftCdfFrac_ == r.leftCdfFrac_ && cdf0Left_ == r.cdf0Left_ && cdf0Right_ == r.cdf0Right_; } bool BifurcatedGauss1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, leftSigma_); gs::write_pod(os, rightSigma_); gs::write_pod(os, nSigmasLeft_); gs::write_pod(os, nSigmasRight_); gs::write_pod(os, norm_); gs::write_pod(os, leftCdfFrac_); gs::write_pod(os, cdf0Left_); gs::write_pod(os, cdf0Right_); return !os.fail(); } BifurcatedGauss1D::BifurcatedGauss1D(const double location, const double scale) : AbsScalableDistribution1D(location, scale) { } BifurcatedGauss1D* BifurcatedGauss1D::read( const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { BifurcatedGauss1D* ptr = new BifurcatedGauss1D(location, scale); gs::read_pod(in, &ptr->leftSigma_); gs::read_pod(in, &ptr->rightSigma_); gs::read_pod(in, &ptr->nSigmasLeft_); gs::read_pod(in, &ptr->nSigmasRight_); gs::read_pod(in, &ptr->norm_); gs::read_pod(in, &ptr->leftCdfFrac_); gs::read_pod(in, &ptr->cdf0Left_); gs::read_pod(in, &ptr->cdf0Right_); if (!in.fail() && ptr->leftSigma_ >= 0.0 && ptr->rightSigma_ >= 0.0 && ptr->leftSigma_ + ptr->rightSigma_ > 0.0 && ptr->nSigmasLeft_ >= 0.0 && ptr->nSigmasRight_ >= 0.0 && ptr->nSigmasLeft_ + ptr->nSigmasRight_ > 0.0 && ptr->norm_ > 0.0 && ptr->leftCdfFrac_ >= 0.0 && ptr->cdf0Left_ >= 0.0 && ptr->cdf0Right_ > ptr->cdf0Left_) return ptr; else delete ptr; } distributionReadError(in, classname()); return 0; } Cauchy1D::Cauchy1D(const double location, const double scale, const std::vector& /* params */) : AbsScalableDistribution1D(location, scale), support_(sqrt(DBL_MAX/M_PI)) { } Cauchy1D::Cauchy1D(const double location, const double scale) : AbsScalableDistribution1D(location, scale), support_(sqrt(DBL_MAX/M_PI)) { } bool Cauchy1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Cauchy1D* Cauchy1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Cauchy1D(location, scale); } double Cauchy1D::unscaledDensity(const double x) const { if (fabs(x) < support_) return 1.0/M_PI/(1.0 + x*x); else return 0.0; } double Cauchy1D::unscaledCdf(const double x) const { if (x < -support_) return 0.0; else if (x > support_) return 1.0; else return atan(x)/M_PI + 0.5; } double Cauchy1D::unscaledQuantile(const double x) const { if (!(x >= 0.0 && x <= 1.0)) throw std::domain_error( "In npstat::Cauchy1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (x == 0.0) return -support_; else if (x == 1.0) return support_; else return tan(M_PI*(x - 0.5)); } bool LogNormal::isEqual(const AbsDistribution1D& otherBase) const { const LogNormal& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && skew_ == r.skew_; } bool LogNormal::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, skew_); return !os.fail(); } LogNormal* LogNormal::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, skew; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &skew); if (!in.fail()) return new LogNormal(location, scale, skew); } distributionReadError(in, classname()); return 0; } void LogNormal::initialize() { w_ = 1.0; logw_ = 0.0; s_ = 0.0; xi_ = 0.0; emgamovd_ = 0.0; if (skew_) { const double b1 = skew_*skew_; const double tmp = pow((2.0+b1+sqrt(b1*(4.0+b1)))/2.0, 1.0/3.0); w_ = tmp + 1.0/tmp - 1.0; logw_ = log(w_); if (logw_ > 0.0) { s_ = sqrt(logw_); emgamovd_ = 1.0/sqrt(w_*(w_-1.0)); xi_ = -emgamovd_*sqrt(w_); } else { // This is not different from a Gaussian within // the numerical precision of our calculations w_ = 1.0; logw_ = 0.0; skew_ = 0.0; } } } LogNormal::LogNormal(const double mean, const double stdev, const double i_skewness) : AbsScalableDistribution1D(mean, stdev), skew_(i_skewness) { initialize(); } LogNormal::LogNormal(const double mean, const double stdev, const std::vector& params) : AbsScalableDistribution1D(mean, stdev), skew_(params[0]) { initialize(); } bool LogNormal::isGaussian() const { return !skew_; } double LogNormal::getDelta() const { if (isGaussian()) throw std::runtime_error( "In npstat::LogNormal::getDelta: this lognormal is " "degenerate and looks like a Gaussian"); return 1.0/s_; } double LogNormal::getGamma() const { if (isGaussian()) throw std::runtime_error( "In npstat::LogNormal::getGamma: this lognormal is " "degenerate and looks like a Gaussian"); const double sig = this->scale(); return log(w_*(w_ - 1.0)/sig/sig)/2.0/s_; } double LogNormal::getXi() const { double loc = this->location(); if (skew_ < 0.0) loc *= -1.0; return xi_*this->scale() + loc; } double LogNormal::unscaledDensity(const double x) const { if (skew_) { const double diff = skew_ > 0.0 ? x - xi_ : -x - xi_; if (diff <= 0.0) return 0.0; else { const double lg = log(diff/emgamovd_); return exp(-lg*lg/2.0/logw_)/s_/SQR2PI/diff; } } else { // This is a Gaussian return exp(-x*x/2.0)/SQR2PI; } } double LogNormal::unscaledCdf(const double x) const { if (skew_) { const double diff = skew_ > 0.0 ? x - xi_ : -x - xi_; double posCdf = 0.0; if (diff > 0.0) posCdf = (1.0 + erf(log(diff/emgamovd_)/s_/SQRT2))/2.0; return skew_ > 0.0 ? posCdf : 1.0 - posCdf; } else return (1.0 + erf(x/SQRT2))/2.0; } double LogNormal::unscaledExceedance(const double x) const { return 1.0 - unscaledCdf(x); } double LogNormal::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::LogNormal::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); const double g = inverseGaussCdf(skew_ >= 0.0 ? r1 : 1.0 - r1); if (skew_) { const double v = emgamovd_*exp(s_*g) + xi_; return skew_ > 0.0 ? v : -v; } else return g; } Moyal1D::Moyal1D(const double location, const double scale) : AbsScalableDistribution1D(location, scale), xmax_(-2.0*log(DBL_MIN*SQR2PI)), xmin_(-log(xmax_)) { } Moyal1D::Moyal1D(const double location, const double scale, const std::vector& /* params */) : AbsScalableDistribution1D(location, scale), xmax_(-2.0*log(DBL_MIN*SQR2PI)), xmin_(-log(xmax_)) { } double Moyal1D::unscaledDensity(const double x) const { if (x <= xmin_ || x >= xmax_) return 0.0; else return exp(-0.5*(x + exp(-x)))/SQR2PI; } double Moyal1D::unscaledCdf(const double x) const { if (x <= xmin_) return 0.0; else if (x >= xmax_) return 1.0; else return incompleteGammaC(0.5, 0.5*exp(-x)); } double Moyal1D::unscaledExceedance(const double x) const { if (x <= xmin_) return 1.0; else if (x >= xmax_) return 0.0; else return incompleteGamma(0.5, 0.5*exp(-x)); } double Moyal1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Moyal1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return xmin_; else if (r1 == 1.0) return xmax_; else { const double d = inverseIncompleteGammaC(0.5, r1); return -log(2.0*d); } } bool Moyal1D::write(std::ostream& os) const { return AbsScalableDistribution1D::write(os); } Moyal1D* Moyal1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (!AbsScalableDistribution1D::read(in, &location, &scale)) { distributionReadError(in, classname()); return 0; } return new Moyal1D(location, scale); } bool Pareto1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, c_); return !os.fail(); } Pareto1D* Pareto1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, c; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &c); if (!in.fail()) return new Pareto1D(location, scale, c); } distributionReadError(in, classname()); return 0; } bool Pareto1D::isEqual(const AbsDistribution1D& otherBase) const { const Pareto1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && c_ == r.c_; } void Pareto1D::initialize() { if (c_ <= 0.0) throw std::invalid_argument( "In npstat::Pareto1D::initialize: power parameter must be positive"); if (c_ > 1.0) support_ = pow(1.0/DBL_MIN, 1.0/c_); else support_ = 1.0/DBL_MIN; } Pareto1D::Pareto1D(const double location, const double scale, const double powerParam) : AbsScalableDistribution1D(location, scale), c_(powerParam) { initialize(); } Pareto1D::Pareto1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), c_(params[0]) { initialize(); } double Pareto1D::unscaledDensity(const double x) const { if (x < 1.0 || x > support_) return 0.0; else return c_/pow(x, c_ + 1.0); } double Pareto1D::unscaledCdf(const double x) const { if (x <= 1.0) return 0.0; else if (x >= support_) return 1.0; else return 1.0 - 1.0/pow(x, c_); } double Pareto1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Pareto1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 1.0; else if (r1 == 1.0) return support_; else return pow(1.0 - r1, -1.0/c_); } double Pareto1D::unscaledExceedance(const double x) const { if (x <= 1.0) return 1.0; else if (x >= support_) return 0.0; else return 1.0/pow(x, c_); } bool UniPareto1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, c_); return !os.fail(); } UniPareto1D* UniPareto1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, c; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &c); if (!in.fail()) return new UniPareto1D(location, scale, c); } distributionReadError(in, classname()); return 0; } bool UniPareto1D::isEqual(const AbsDistribution1D& otherBase) const { const UniPareto1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && c_ == r.c_; } void UniPareto1D::initialize() { if (c_ <= 0.0) throw std::invalid_argument( "In npstat::UniPareto1D::initialize: power parameter must be positive"); if (c_ > 1.0) support_ = pow(1.0/DBL_MIN, 1.0/c_); else support_ = 1.0/DBL_MIN; amplitude_ = c_/(c_ + 1.0); } UniPareto1D::UniPareto1D(const double location, const double scale, const double powerParam) : AbsScalableDistribution1D(location, scale), c_(powerParam) { initialize(); } UniPareto1D::UniPareto1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), c_(params[0]) { initialize(); } double UniPareto1D::unscaledDensity(const double x) const { if (x < 0.0 || x > support_) return 0.0; else if (x <= 1.0) return amplitude_; else return amplitude_/pow(x, c_ + 1.0); } double UniPareto1D::unscaledCdf(const double x) const { if (x <= 0.0) return 0.0; else if (x >= support_) return 1.0; else if (x <= 1.0) return x*amplitude_; else return amplitude_ + (1.0 - amplitude_)*(1.0 - 1.0/pow(x, c_)); } double UniPareto1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::UniPareto1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 <= amplitude_) return r1/amplitude_; else if (r1 == 1.0) return support_; else return pow(1.0 - (r1 - amplitude_)/(1.0 - amplitude_), -1.0/c_); } double UniPareto1D::unscaledExceedance(const double x) const { if (x > 1.0) return (1.0 - amplitude_)/pow(x, c_); else return 1.0 - unscaledCdf(x); } bool Huber1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, tailWeight_); return !os.fail(); } Huber1D* Huber1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale, tailWeight; if (AbsScalableDistribution1D::read(in, &location, &scale)) { gs::read_pod(in, &tailWeight); if (!in.fail()) return new Huber1D(location, scale, tailWeight); } distributionReadError(in, classname()); return 0; } bool Huber1D::isEqual(const AbsDistribution1D& otherBase) const { const Huber1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && tailWeight_ == r.tailWeight_; } void Huber1D::initialize() { if (!(tailWeight_ >= 0.0 && tailWeight_ < 1.0)) throw std::invalid_argument( "In npstat::Huber1D::initialize: " "tail weight not inside [0, 1) interval"); if (tailWeight_ == 0.0) { // Pure Gaussian a_ = DBL_MAX; normfactor_ = 1.0/sqrt(2.0*M_PI); support_ = inverseGaussCdf(1.0); cdf0_ = 0.0; } else { // Solve the equation for "a" by bisection const double eps = 2.0*DBL_EPSILON; double c = -2.0*log(tailWeight_); assert(c > 0.0); assert(weight(c) <= tailWeight_); double b = 0.0; while ((c - b)/(c + b) > eps) { const double half = (c + b)/2.0; if (weight(half) >= tailWeight_) b = half; else c = half; } a_ = (c + b)/2.0; normfactor_ = 0.5/(exp(-a_*a_/2.0)/a_ + sqrt(M_PI/2.0)*erf(a_/SQRT2)); support_ = a_/2.0 - log(DBL_MIN)/a_; cdf0_ = (1.0 + erf(-a_/SQRT2))/2.0; } } Huber1D::Huber1D(const double location, const double scale, const double tailWeight) : AbsScalableDistribution1D(location, scale), tailWeight_(tailWeight) { initialize(); } Huber1D::Huber1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), tailWeight_(params[0]) { initialize(); } double Huber1D::unscaledDensity(const double x) const { const double absx = fabs(x); if (absx <= a_) return normfactor_*exp(-x*x/2.0); else return normfactor_*exp(a_*(a_/2.0 - absx)); } double Huber1D::unscaledCdf(const double x) const { if (tailWeight_ == 0.0) return (1.0 + erf(x/SQRT2))/2.0; if (x < -a_) return normfactor_*exp((a_*(a_ + 2*x))/2.0)/a_; else if (x <= a_) { static const double sq1 = sqrt(M_PI/2.0); static const double sq2 = sqrt(2.0); return normfactor_*sq1*(erf(a_/sq2) + erf(x/sq2)) + tailWeight_/2; } else return 1.0 - normfactor_*exp((a_*(a_ - 2*x))/2.0)/a_; } double Huber1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Huber1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (tailWeight_ == 0.0) return inverseGaussCdf(r1); if (r1 == 0.0) return -support_; else if (r1 == 1.0) return support_; else if (r1 <= tailWeight_/2.0) return log(r1*a_/normfactor_)/a_ - 0.5*a_; else if (r1 < 1.0 - tailWeight_/2.0) { const double t = (r1 - tailWeight_/2.0)/normfactor_/SQR2PI + cdf0_; return inverseGaussCdf(t); } else return 0.5*a_ - log((1.0 - r1)*a_/normfactor_)/a_; } double Huber1D::weight(const double a) const { static const double sq1 = sqrt(M_PI/2.0); return 1.0/(1.0 + a*exp(a*a/2.0)*sq1*erf(a/SQRT2)); } bool Tabulated1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, deg_); gs::write_pod_vector(os, table_); return !os.fail(); } Tabulated1D* Tabulated1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { unsigned deg = 0; gs::read_pod(in, °); std::vector table; gs::read_pod_vector(in, &table); if (!in.fail() && table.size()) return new Tabulated1D(location, scale, &table[0], table.size(), deg); } distributionReadError(in, classname()); return 0; } bool Tabulated1D::isEqual(const AbsDistribution1D& otherBase) const { const Tabulated1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && table_ == r.table_ && deg_ == r.deg_; } Tabulated1D::Tabulated1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale) { const unsigned npara = params.size(); if (npara) initialize(¶ms[0], npara, std::min(3U, npara-1U)); else initialize(static_cast(0), 0U, 0U); } void Tabulated1D::normalize() { cdf_.clear(); cdf_.reserve(len_); cdf_.push_back(0.0); long double sum = 0.0L; for (unsigned i=0; i(sum)); } double norm = cdf_[len_ - 1]; assert(norm > 0.0); for (unsigned i=0; i0; --i) { sum += intervalInteg(i-1); exceed_[i-1] = static_cast(sum); } norm = exceed_[0]; for (unsigned i=0; i 1.0) return 0.0; if (x == 0.0) return table_[0]; if (x == 1.0) return table_[len_ - 1]; unsigned idx = static_cast(x/step_); if (idx >= len_ - 1) idx = len_ - 2; const double dx = x/step_ - idx; switch (deg_) { case 0: if (dx < 0.5) return table_[idx]; else return table_[idx + 1]; case 1: return interpolate_linear(dx, table_[idx], table_[idx + 1]); case 2: if (idx == 0) return interpolate_quadratic(dx, table_[idx], table_[idx + 1], table_[idx + 2]); else if (idx == len_ - 2) return interpolate_quadratic(dx+1.0, table_[idx - 1], table_[idx], table_[idx + 1]); else { const double v0 = interpolate_quadratic( dx, table_[idx], table_[idx + 1], table_[idx + 2]); const double v1 = interpolate_quadratic( dx+1.0, table_[idx - 1], table_[idx], table_[idx + 1]); return (v0 + v1)/2.0; } case 3: if (idx == 0) return interpolate_cubic(dx, table_[idx], table_[idx + 1], table_[idx + 2], table_[idx + 3]); else if (idx == len_ - 2) return interpolate_cubic(dx+2.0, table_[idx-2], table_[idx-1], table_[idx], table_[idx + 1]); else return interpolate_cubic(dx+1.0, table_[idx - 1], table_[idx], table_[idx + 1], table_[idx + 2]); default: assert(0); return 0.0; } } double Tabulated1D::unscaledDensity(const double x) const { const double v = this->interpolate(x); if (v >= 0.0) return v; else return 0.0; } double Tabulated1D::unscaledCdf(double x) const { if (x <= 0.0) return 0.0; if (x >= 1.0) return 1.0; unsigned idx = static_cast(x/step_); if (idx >= len_ - 1) idx = len_ - 2; double v; switch (deg_) { case 0: { const double dx = x/step_ - idx; if (dx < 0.5) v = table_[idx]*dx*step_; else v = (table_[idx]*0.5 + table_[idx + 1]*(dx - 0.5))*step_; } break; default: v = interpIntegral(step_*idx, x); } return cdf_[idx] + v; } double Tabulated1D::unscaledExceedance(double x) const { if (x <= 0.0) return 1.0; if (x >= 1.0) return 0.0; unsigned idx = static_cast(x/step_); if (idx >= len_ - 1) idx = len_ - 2; double v; switch (deg_) { case 0: { const double dx = x/step_ - idx; if (dx < 0.5) v = table_[idx]*dx*step_; else v = (table_[idx]*0.5 + table_[idx + 1]*(dx - 0.5))*step_; } break; default: v = interpIntegral(step_*idx, x); } return exceed_[idx] - v; } double Tabulated1D::interpIntegral(const double a, const double b) const { static const double legendreRootOver2 = 0.5/sqrt(3.0); const double x0 = (b + a)/2.0; const double step = b - a; const double v0 = unscaledDensity(x0 - legendreRootOver2*step); const double v1 = unscaledDensity(x0 + legendreRootOver2*step); return step*(v0 + v1)/2.0; } double Tabulated1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::Tabulated1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0) return 0.0; if (r1 == 1.0) return 1.0; unsigned idx = std::lower_bound(cdf_.begin(), cdf_.end(), r1) - cdf_.begin() - 1U; double xlo = step_*idx; const double dcdf = r1 - cdf_[idx]; assert(dcdf > 0.0); switch (deg_) { case 0: { const double c1 = table_[idx]*0.5*step_; if (dcdf <= c1) { assert(table_[idx] > 0.0); return xlo + dcdf/table_[idx]; } else { assert(table_[idx+1] > 0.0); return xlo + 0.5*step_ + (dcdf - c1)/table_[idx+1]; } } case 1: { const double a = (table_[idx+1] - table_[idx])/step_/2.0; if (a == 0.0) { assert(table_[idx] > 0.0); return xlo + dcdf/table_[idx]; } else { double x1, x2; const unsigned nroots = solveQuadratic( table_[idx]/a, -dcdf/a, &x1, &x2); if (nroots != 2U) throw std::runtime_error( "In npstat::Tabulated1D::unscaledQuantile: " "unexpected number of solutions"); if (fabs(x1 - 0.5*step_) < fabs(x2 - 0.5*step_)) return xlo + x1; else return xlo + x2; } } default: { double xhi = xlo + step_; const double eps = 2.0*DBL_EPSILON; while ((xhi - xlo)/(xhi + xlo) > eps) { const double med = (xhi + xlo)/2.0; if (unscaledCdf(med) >= r1) xhi = med; else xlo = med; } return (xhi + xlo)/2.0; } } } bool BinnedDensity1D::isEqual(const AbsDistribution1D& otherBase) const { const double eps = 1.0e-12; const BinnedDensity1D& r = static_cast(otherBase); if (!AbsScalableDistribution1D::isEqual(r)) return false; if (!(deg_ == r.deg_)) return false; const unsigned long n = table_.size(); if (!(n == r.table_.size())) return false; for (unsigned long i=0; i eps) return false; return true; } BinnedDensity1D::BinnedDensity1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale) { const unsigned npara = params.size(); if (npara) initialize(¶ms[0], npara, std::min(1U, npara-1U)); else initialize(static_cast(0), 0U, 0U); } void BinnedDensity1D::normalize() { cdf_.clear(); cdf_.reserve(len_); long double sum = 0.0L; switch (deg_) { case 0U: for (unsigned i=0; i(sum)); } break; case 1U: { double oldval = 0.0; double* data = &table_[0]; for (unsigned i=0; i(sum)); } sum += oldval*0.5; } break; default: assert(0); } const double norm = static_cast(sum); assert(norm > 0.0); const double integ = norm*step_; for (unsigned i=0; i 1.0) return 0.0; switch (deg_) { case 0: { unsigned idx = static_cast(x/step_); if (idx > len_ - 1) idx = len_ - 1; return table_[idx]; } case 1: { const double xs = x - step_/2.0; if (xs <= 0.0) return table_[0]; const unsigned idx = static_cast(xs/step_); if (idx > len_ - 2) return table_[len_ - 1]; const double dx = xs/step_ - idx; return interpolate_linear(dx, table_[idx], table_[idx + 1]); } default: assert(0); return 0.0; } } double BinnedDensity1D::unscaledCdf(double x) const { if (x <= 0.0) return 0.0; if (x >= 1.0) return 1.0; double v = 0.0; switch (deg_) { case 0: { unsigned idx = static_cast(x/step_); if (idx > len_ - 1) idx = len_ - 1; v = (idx ? cdf_[idx - 1] : 0.0) + table_[idx]*(x - idx*step_); } break; case 1: { const double xs = x - step_/2.0; if (xs <= 0.0) v = table_[0]*x; else { const unsigned idx = static_cast(xs/step_); if (idx > len_ - 2) v = 1.0 - table_[len_ - 1]*(1.0 - x); else { const double dx = xs - idx*step_; const double slope = (table_[idx+1] - table_[idx])/step_; v = cdf_[idx] + table_[idx]*dx + slope*dx*dx/2.0; } } } break; default: assert(0); break; } if (v < 0.0) v = 0.0; else if (v > 1.0) v = 1.0; return v; } double BinnedDensity1D::unscaledQuantile(const double r1) const { if (!(r1 >= 0.0 && r1 <= 1.0)) throw std::domain_error( "In npstat::BinnedDensity1D::unscaledQuantile: " "cdf argument outside of [0, 1] interval"); if (r1 == 0.0 && deg_) return 0.0; if (r1 == 1.0 && deg_) return 1.0; double v = 0.0; switch (deg_) { case 0: { if (r1 == 0) v = firstNonZeroBin_; else if (r1 == 1.0) v = lastNonZeroBin_ + 1U; else if (r1 <= cdf_[0]) v = r1/cdf_[0]; else { double rem; const unsigned bin = quantileBinFromCdf(&cdf_[0],len_,r1,&rem) + 1U; assert(bin < len_); v = bin + rem; } } break; case 1: { if (r1 <= cdf_[0]) v = 0.5*r1/cdf_[0]; else if (r1 >= cdf_[len_ - 1]) v = (len_-0.5+0.5*(r1-cdf_[len_-1])/(1.0-cdf_[len_-1])); else { const unsigned idx = std::lower_bound(cdf_.begin(),cdf_.end(),r1) - cdf_.begin() - 1U; assert(idx < len_ - 1); const double k = (table_[idx+1] - table_[idx])/step_; const double y = r1 - cdf_[idx]; double x; if (fabs(k) < 1.e-10*table_[idx]) x = y/table_[idx]; else { const double b = 2.0*table_[idx]/k; const double c = -2.0*y/k; double x1, x2; if (solveQuadratic(b, c, &x1, &x2)) { if (fabs(x1 - step_*0.5) < fabs(x2 - step_*0.5)) x = x1; else x = x2; } else { // This can happen due to various round-off problems. // Assume that the quadratic equation determinant // should have been 0 instead of negative. x = -b/2.0; } } if (x < 0.0) x = 0.0; else if (x > step_) x = step_; v = x/step_ + idx + 0.5; } } break; default: assert(0); return 0.0; } v *= step_; if (v < 0.0) v = 0.0; else if (v > 1.0) v = 1.0; return v; } bool BinnedDensity1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, deg_); gs::write_pod_vector(os, table_); return !os.fail(); } BinnedDensity1D* BinnedDensity1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current( gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { unsigned deg = 0; gs::read_pod(in, °); std::vector table; gs::read_pod_vector(in, &table); if (!in.fail() && table.size()) return new BinnedDensity1D(location, scale, &table[0], table.size(), deg); } distributionReadError(in, classname()); return 0; } bool StudentsT1D::write(std::ostream& os) const { AbsScalableDistribution1D::write(os); gs::write_pod(os, nDoF_); return !os.fail(); } StudentsT1D* StudentsT1D::read(const gs::ClassId& id, std::istream& in) { static const gs::ClassId current(gs::ClassId::makeId()); current.ensureSameId(id); double location, scale; if (AbsScalableDistribution1D::read(in, &location, &scale)) { double nDoF; gs::read_pod(in, &nDoF); if (!in.fail()) return new StudentsT1D(location, scale, nDoF); } distributionReadError(in, classname()); return 0; } bool StudentsT1D::isEqual(const AbsDistribution1D& otherBase) const { const StudentsT1D& r = static_cast(otherBase); return AbsScalableDistribution1D::isEqual(r) && nDoF_ == r.nDoF_; } StudentsT1D::StudentsT1D(const double location, const double scale, const double degreesOfFreedom) : AbsScalableDistribution1D(location, scale), nDoF_(degreesOfFreedom) { initialize(); } StudentsT1D::StudentsT1D(const double location, const double scale, const std::vector& params) : AbsScalableDistribution1D(location, scale), nDoF_(params[0]) { initialize(); } void StudentsT1D::initialize() { if (nDoF_ <= 0.0) throw std::invalid_argument( "In npstat::StudentsT1D::initialize: invalid number " "of degrees of freedom"); power_ = (nDoF_ + 1.0)/2.0; bignum_ = 0.0; normfactor_ = Gamma(power_)/Gamma(nDoF_/2.0)/sqrt(nDoF_)/SQRPI; } double StudentsT1D::unscaledDensity(const double x) const { return normfactor_*pow(1.0 + x*x/nDoF_, -power_); } double StudentsT1D::unscaledCdf(const double t) const { const double s = sqrt(t*t + nDoF_); return incompleteBeta(nDoF_/2.0, nDoF_/2.0, (t + s)/(2.0*s)); } double StudentsT1D::unscaledExceedance(const double x) const { return 1.0 - unscaledCdf(x); } double StudentsT1D::unscaledQuantile(const double r1) const { if (r1 == 0.5) return 0.0; const double c = inverseIncompleteBeta(nDoF_/2.0, nDoF_/2.0, r1); const double tmp = 2.0*c - 1.0; const double a = tmp*tmp; const double denom = 1.0 - a; if (denom > 0.0) { const double sqroot = sqrt(nDoF_*a/denom); return r1 > 0.5 ? sqroot : -sqroot; } else { if (bignum_ == 0.0) (const_cast(this))->bignum_ = effectiveSupport(); return r1 > 0.5 ? bignum_ : -bignum_; } } double StudentsT1D::effectiveSupport() const { // Figure out at which (positive) values of the argument // the density becomes effectively indistinguishable from 0 const double biglog = (log(normfactor_) - log(DBL_MIN) + power_*log(nDoF_))/(2.0*power_); if (biglog >= log(DBL_MAX)) return DBL_MAX; else return exp(biglog); } } Index: trunk/npstat/stat/Makefile =================================================================== --- trunk/npstat/stat/Makefile (revision 860) +++ trunk/npstat/stat/Makefile (revision 861) @@ -1,1809 +1,1807 @@ # Makefile.in generated by automake 1.16.5 from Makefile.am. # npstat/stat/Makefile. 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LognormalTransform1D.cc JohnsonOrthoPoly1D.cc johnsonIntegral.cc include_HEADERS = AbsBandwidthCV.hh \ AbsBandwidthGCV.hh \ AbsBinnedComparison1D.hh \ AbsBinnedComparison1D.icc \ AbsCGF1D.hh \ AbsCompositeDistroBuilder.hh \ AbsCompositeDistroBuilder.icc \ AbsCopulaSmootherBase.hh \ AbsCopulaSmootherBase.icc \ AbsCVCopulaSmoother.hh \ AbsDiscreteDistribution1D.hh \ AbsDistribution1D.hh \ AbsDistribution1D.icc \ AbsDistributionND.hh \ AbsDistributionND.icc \ AbsDistributionTransform1D.hh \ AbsDistro1DBuilder.hh \ AbsDistro1DBuilder.icc \ AbsFilter1DBuilder.hh \ AbsInterpolatedDistribution1D.hh \ AbsInterpolationAlgoND.hh \ AbsKDE1DKernel.hh \ AbsKDE1DKernel.icc \ AbsLossCalculator.hh \ AbsMarginalSmootherBase.hh \ AbsMarginalSmootherBase.icc \ AbsNtuple.hh \ AbsNtuple.icc \ AbsPolyFilter1D.hh \ AbsPolyFilterND.hh \ AbsResponseBoxBuilder.hh \ AbsResponseIntervalBuilder.hh \ AbsSmoothGOFTest1D.hh \ AbsSmoothGOFTest1D.icc \ AbsSymbetaFilterProvider.hh \ AbsTwoDistros1DFunctor.hh \ AbsUnbinnedGOFTest1D.hh \ AbsUnfold1D.hh \ AbsUnfoldingFilterND.hh \ AbsUnfoldND.hh \ AllSymbetaParams1D.hh \ amiseOptimalBandwidth.hh \ amiseOptimalBandwidth.icc \ ArchivedNtuple.hh \ ArchivedNtuple.icc \ ArrayProjectors.hh \ ArrayProjectors.icc \ arrayStats.hh \ arrayStats.icc \ AsinhTransform1D.hh \ BandwidthCVLeastSquares1D.hh \ BandwidthCVLeastSquares1D.icc \ BandwidthCVLeastSquaresND.hh \ BandwidthCVLeastSquaresND.icc \ BandwidthCVPseudoLogli1D.hh \ BandwidthCVPseudoLogli1D.icc \ BandwidthCVPseudoLogliND.hh \ BandwidthCVPseudoLogliND.icc \ BandwidthGCVLeastSquares1D.hh \ BandwidthGCVLeastSquares1D.icc \ BandwidthGCVLeastSquaresND.hh \ BandwidthGCVLeastSquaresND.icc \ BandwidthGCVPseudoLogli1D.hh \ BandwidthGCVPseudoLogli1D.icc \ BandwidthGCVPseudoLogliND.hh \ BandwidthGCVPseudoLogliND.icc \ BernsteinCopulaSmoother.hh \ BernsteinFilter1DBuilder.hh \ betaKernelsBandwidth.hh \ betaKernelsBandwidth.icc \ BetaFilter1DBuilder.hh \ BinnedADTest1D.hh \ BinnedKSTest1D.hh \ BinSummary.hh \ BinSummary.icc \ bivariateChiSquare.hh \ BoundaryHandling.hh \ BoundaryMethod.hh \ buildInterpolatedCompositeDistroND.hh \ buildInterpolatedCompositeDistroND.icc \ buildInterpolatedDistro1DNP.hh \ buildInterpolatedDistro1DNP.icc \ buildInterpolatedHelpers.hh \ CensoredQuantileRegression.hh \ CensoredQuantileRegression.icc \ CircularBuffer.hh \ CircularBuffer.icc \ Column.hh \ Column.icc \ ComparisonDistribution1D.hh \ CompositeDistribution1D.hh \ CompositeDistributionND.hh \ CompositeDistributionND.icc \ CompositeDistros1D.hh \ ConstantBandwidthSmoother1D.hh \ ConstantBandwidthSmootherND.hh \ ConstantBandwidthSmootherND.icc \ continuousDegreeTaper.hh \ convertAxis.hh \ CopulaInterpolationND.hh \ Copulas.hh \ correctDensityEstimateGHU.hh \ CrossCovarianceAccumulator.hh \ CrossCovarianceAccumulator.icc \ cumulantConversion.hh \ cumulantConversion.icc \ cumulantUncertainties.hh \ cumulantUncertainties.icc \ CVCopulaSmoother.hh \ CVCopulaSmoother.icc \ DeltaMixture1D.hh \ DeltaMixture1D.icc \ DensityAveScanND.hh \ DensityOrthoPoly1D.hh \ DensityScan1D.hh \ DensityScanND.hh \ DiscreteDistribution1DReader.hh \ DiscreteDistributions1D.hh \ DiscreteGauss1DBuilder.hh \ DiscreteGaussCopulaSmoother.hh \ discretizationErrorND.hh \ Distribution1DFactory.hh \ Distribution1DReader.hh \ DistributionTransform1DReader.hh \ DistributionMix1D.hh \ DistributionMixND.hh \ DistributionNDReader.hh \ Distributions1D.hh \ Distributions1D.icc \ DistributionsND.hh \ DistributionsND.icc \ distributionReadError.hh \ distro1DStats.hh \ DualHistoAxis.hh \ DummyCompositeDistroBuilder.hh \ DummyDistro1DBuilder.hh \ DummyResponseBoxBuilder.hh \ DummyResponseIntervalBuilder.hh \ EdgeworthSeries1D.hh \ EdgeworthSeriesMethod.hh \ EllipticalDistribution.hh \ EllipticalDistributions.hh \ empiricalCopula.hh \ empiricalCopulaHisto.hh \ empiricalCopulaHisto.icc \ empiricalCopula.icc \ ExpTiltedDistribution1D.hh \ fillHistoFromText.hh \ fillHistoFromText.icc \ Filter1DBuilders.hh \ FitUtils.hh \ FitUtils.icc \ FoldedDistribution1D.hh \ GaussianMixtureEntry.hh \ GaussianMixture1D.hh \ gaussianResponseMatrix.hh \ GCVCopulaSmoother.hh \ GCVCopulaSmoother.icc \ griddedRobustRegression.hh \ griddedRobustRegression.icc \ GriddedRobustRegressionStop.hh \ GridInterpolatedDistribution.hh \ GridRandomizer.hh \ HistoAxis.hh \ HistoND.hh \ HistoND.icc \ HistoNDCdf.hh \ HistoNDFunctorInstances.hh \ histoStats.hh \ histoStats.icc \ histoUtils.hh \ histoUtils.icc \ IdentityTransform1D.hh \ InMemoryNtuple.hh \ InMemoryNtuple.icc \ InterpolatedDistribution1D.hh \ InterpolatedDistro1D1P.hh \ InterpolatedDistro1DNP.hh \ interpolateHistoND.hh \ interpolateHistoND.icc \ InterpolationFunctorInstances.hh \ JohnsonCurves.hh \ johnsonIntegral.hh \ JohnsonKDESmoother.hh \ JohnsonOrthoPoly1D.hh \ johnsonTransform.hh \ KDE1D.hh \ KDE1DCV.hh \ KDE1DHOSymbetaKernel.hh \ KDECopulaSmoother.hh \ KDECopulaSmoother.icc \ KDEGroupedCopulaSmoother.hh \ KDEGroupedCopulaSmoother.icc \ KDEFilterND.hh \ KDEFilterND.icc \ kendallsTau.hh \ kendallsTau.icc \ - kernelSensitivityMatrix.hh \ - kernelSensitivityMatrix.icc \ LeftCensoredDistribution.hh \ likelihoodStatisticCumulants.hh \ LikelihoodStatisticType.hh \ LocalLogisticRegression.hh \ LocalLogisticRegression.icc \ LocalMultiFilter1D.hh \ LocalMultiFilter1D.icc \ LocalPolyFilter1D.hh \ LocalPolyFilter1D.icc \ LocalPolyFilter1DReader.hh \ LocalPolyFilterND.hh \ LocalPolyFilterND.icc \ LocalQuadraticLeastSquaresND.hh \ LocalQuadraticLeastSquaresND.icc \ LocalQuantileRegression.hh \ LocalQuantileRegression.icc \ LocationScaleFamily1D.hh \ LocationScaleTransform1.hh \ LocationScaleTransform1.icc \ LocScaleTransform1D.hh \ logLikelihoodPeak.hh \ LognormalTransform1D.hh \ LogRatioTransform1D.hh \ LogTransform1D.hh \ LOrPE1D.hh \ LOrPE1D.icc \ LOrPE1DCV.hh \ LOrPE1DCVResult.hh \ LOrPE1DFixedDegreeCVPicker.hh \ LOrPE1DFixedDegreeCVRunner.hh \ LOrPE1DFixedDegreeCVScanner.hh \ LOrPE1DSymbetaKernel.hh \ LOrPE1DSymbetaKernel.icc \ LOrPE1DVariableDegreeCVRunner.hh \ LOrPE1DVariableDegreeCVPicker.hh \ LOrPECopulaSmoother.hh \ LOrPECopulaSmoother.icc \ LOrPEGroupedCopulaSmoother.hh \ LOrPEGroupedCopulaSmoother.icc \ LOrPEMarginalSmoother.hh \ LOrPEWeightsLocalConvergence.hh \ lorpeBackgroundCVDensity1D.hh \ lorpeBackgroundCVDensity1D.icc \ lorpeBackground1D.hh \ lorpeBackground1D.icc \ lorpeMise1D.hh \ lorpeSmooth1D.hh \ lorpeSmooth1D.icc \ make_UnbinnedGOFTest1D.hh \ MatrixFilter1DBuilder.hh \ MemoizingSymbetaFilterProvider.hh \ mergeTwoHistos.hh \ mergeTwoHistos.icc \ mirrorWeight.hh \ ModulatedDistribution1D.hh \ MultiscaleEMUnfold1D.hh \ multinomialCovariance1D.hh \ MultivariateSumAccumulator.hh \ MultivariateSumsqAccumulator.hh \ MultivariateSumsqAccumulator.icc \ MultivariateWeightedSumAccumulator.hh \ MultivariateWeightedSumsqAccumulator.hh \ MultivariateWeightedSumsqAccumulator.icc \ neymanPearsonWindow1D.hh \ NMCombinationSequencer.hh \ NonparametricCompositeBuilder.hh \ NonparametricCompositeBuilder.icc \ NonparametricDistro1DBuilder.hh \ NonparametricDistro1DBuilder.icc \ NtHistoFill.hh \ NtNtupleFill.hh \ NtRectangularCut.hh \ NtRectangularCut.icc \ NtupleBuffer.hh \ NtupleBuffer.icc \ NtupleRecordTypes.hh \ NtupleRecordTypesFwd.hh \ NtupleReference.hh \ ntupleStats.hh \ NUHistoAxis.hh \ OrderedPointND.hh \ OrderedPointND.icc \ orthoPoly1DVProducts.hh \ orthoPoly1DVProducts.icc \ OrthoPolyGOFTest1D.hh \ OrthoPolyGOFTest1D.icc \ OSDE1D.hh \ OSDE1D.icc \ PearsonsChiSquared.hh \ polyExpectation.hh \ polyExpectation.icc \ PolyFilterCollection1D.hh \ PolynomialDistro1D.hh \ PowerHalfCauchy1D.hh \ PowerRatio1D.hh \ PowerTransform1D.hh \ productResponseMatrix.hh \ productResponseMatrix.icc \ ProductSymmetricBetaNDCdf.hh \ QuadraticOrthoPolyND.hh \ QuadraticOrthoPolyND.icc \ QuantileRegression1D.hh \ QuantileRegression1D.icc \ QuantileTable1D.hh \ randomHistoFill1D.hh \ randomHistoFill1D.icc \ randomHistoFillND.hh \ randomHistoFillND.icc \ RatioOfNormals.hh \ RatioResponseBoxBuilder.hh \ RatioResponseBoxBuilder.icc \ RatioResponseIntervalBuilder.hh \ RatioResponseIntervalBuilder.icc \ ResponseMatrix.hh \ RightCensoredDistribution.hh \ saddlepointDistribution1D.hh \ SampleAccumulator.hh \ SampleAccumulator.icc \ sampleDistro1DWithWeight.hh \ SbMomentsCalculator.hh \ ScalableGaussND.hh \ scannedKSDistance.hh \ scanMultivariateDensityAsWeight.hh \ scanSymmetricDensityAsWeight.hh \ semiMixLocalLogLikelihood.hh \ SequentialCopulaSmoother.hh \ SequentialCopulaSmoother.icc \ SequentialGroupedCopulaSmoother.hh \ SequentialGroupedCopulaSmoother.icc \ SequentialPolyFilterND.hh \ SequentialPolyFilterND.icc \ SeriesCGF1D.hh \ SineGOFTest1D.hh \ SineGOFTest1D.icc \ SinhAsinhTransform1D.hh \ SmoothCDGOFTest1D.hh \ SmoothCDGOFTest1D.icc \ SmoothedEMUnfold1D.hh \ SmoothedEMUnfoldND.hh \ SmoothGOFTest1D.hh \ SmoothGOFTest1D.icc \ solveForLOrPEWeights.hh \ spearmansRho.hh \ spearmansRho.icc \ StatAccumulator.hh \ StatAccumulatorArr.hh \ StatAccumulatorPair.hh \ statUncertainties.hh \ StatUtils.hh \ StatUtils.icc \ StorableHistoNDFunctor.hh \ StorableHistoNDFunctor.icc \ StorableInterpolationFunctor.hh \ StorableInterpolationFunctor.icc \ StorableMultivariateFunctor.hh \ StorableMultivariateFunctorReader.hh \ SymbetaParams1D.hh \ TransformedDistribution1D.hh \ TruncatedDistribution1D.hh \ TwoDistros1DFunctors.hh \ TwoPointsLTSLoss.hh \ TwoPointsLTSLoss.icc \ UGaussConvolution1D.hh \ UnbinnedGOFTests1D.hh \ UnbinnedGOFTests1D.icc \ UnbinnedGOFTest1DFactory.hh \ UnfoldingBandwidthScanner1D.hh \ UnfoldingBandwidthScannerND.hh \ UnfoldingFilterNDReader.hh \ UnitMapInterpolationND.hh \ variableBandwidthSmooth1D.hh \ variableBandwidthSmooth1D.icc \ VariableBandwidthSmoother1D.hh \ VerticallyInterpolatedDistribution1D.hh \ volumeDensityFromBinnedRadial.hh \ weightedCopulaHisto.hh \ weightedCopulaHisto.icc \ WeightedDistro1DPtr.hh \ WeightedLTSLoss.hh \ WeightedLTSLoss.icc \ WeightedSampleAccumulator.hh \ WeightedSampleAccumulator.icc \ WeightedStatAccumulator.hh \ 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./$(DEPDIR)/LocalPolyFilter1DReader.Plo -rm -f ./$(DEPDIR)/LocationScaleFamily1D.Plo -rm -f ./$(DEPDIR)/LogRatioTransform1D.Plo -rm -f ./$(DEPDIR)/LogTransform1D.Plo -rm -f ./$(DEPDIR)/LognormalTransform1D.Plo -rm -f ./$(DEPDIR)/MatrixFilter1DBuilder.Plo -rm -f ./$(DEPDIR)/MemoizingSymbetaFilterProvider.Plo -rm -f ./$(DEPDIR)/MultiscaleEMUnfold1D.Plo -rm -f ./$(DEPDIR)/NMCombinationSequencer.Plo -rm -f ./$(DEPDIR)/NUHistoAxis.Plo -rm -f ./$(DEPDIR)/OSDE1D.Plo -rm -f ./$(DEPDIR)/OrthoPolyGOFTest1D.Plo -rm -f ./$(DEPDIR)/PearsonsChiSquared.Plo -rm -f ./$(DEPDIR)/PolyFilterCollection1D.Plo -rm -f ./$(DEPDIR)/PolynomialDistro1D.Plo -rm -f ./$(DEPDIR)/PowerHalfCauchy1D.Plo -rm -f ./$(DEPDIR)/PowerRatio1D.Plo -rm -f ./$(DEPDIR)/PowerTransform1D.Plo -rm -f ./$(DEPDIR)/ProductSymmetricBetaNDCdf.Plo -rm -f ./$(DEPDIR)/QuadraticOrthoPolyND.Plo -rm -f ./$(DEPDIR)/QuantileTable1D.Plo -rm -f ./$(DEPDIR)/RatioOfNormals.Plo -rm -f ./$(DEPDIR)/ResponseMatrix.Plo -rm -f ./$(DEPDIR)/RightCensoredDistribution.Plo -rm -f ./$(DEPDIR)/SbMomentsBigGamma.Plo -rm -f ./$(DEPDIR)/SbMomentsCalculator.Plo -rm -f ./$(DEPDIR)/ScalableGaussND.Plo -rm -f ./$(DEPDIR)/SequentialCopulaSmoother.Plo -rm -f ./$(DEPDIR)/SequentialGroupedCopulaSmoother.Plo -rm -f ./$(DEPDIR)/SequentialPolyFilterND.Plo -rm -f ./$(DEPDIR)/SeriesCGF1D.Plo -rm -f ./$(DEPDIR)/SineGOFTest1D.Plo -rm -f ./$(DEPDIR)/SinhAsinhTransform1D.Plo -rm -f ./$(DEPDIR)/SmoothCDGOFTest1D.Plo -rm -f ./$(DEPDIR)/SmoothedEMUnfold1D.Plo -rm -f ./$(DEPDIR)/SmoothedEMUnfoldND.Plo -rm -f ./$(DEPDIR)/StatAccumulator.Plo -rm -f ./$(DEPDIR)/StatAccumulatorArr.Plo -rm -f ./$(DEPDIR)/StatAccumulatorPair.Plo -rm -f ./$(DEPDIR)/StatUtils.Plo -rm -f ./$(DEPDIR)/StorableMultivariateFunctor.Plo -rm -f ./$(DEPDIR)/StorableMultivariateFunctorReader.Plo -rm -f ./$(DEPDIR)/SymbetaParams1D.Plo -rm -f ./$(DEPDIR)/TransformedDistribution1D.Plo -rm -f ./$(DEPDIR)/TruncatedDistribution1D.Plo -rm -f ./$(DEPDIR)/TwoDistros1DFunctors.Plo -rm -f ./$(DEPDIR)/UGaussConvolution1D.Plo -rm -f ./$(DEPDIR)/UnbinnedGOFTest1DFactory.Plo -rm -f ./$(DEPDIR)/UnbinnedGOFTests1D.Plo -rm -f ./$(DEPDIR)/UnfoldingBandwidthScanner1D.Plo -rm -f ./$(DEPDIR)/UnfoldingBandwidthScannerND.Plo -rm -f ./$(DEPDIR)/UnfoldingFilterNDReader.Plo -rm -f ./$(DEPDIR)/UnitMapInterpolationND.Plo -rm -f ./$(DEPDIR)/VariableBandwidthSmoother1D.Plo -rm -f ./$(DEPDIR)/VerticallyInterpolatedDistribution1D.Plo -rm -f ./$(DEPDIR)/WeightTableFilter1DBuilder.Plo -rm -f ./$(DEPDIR)/WeightedStatAccumulator.Plo -rm -f ./$(DEPDIR)/WeightedStatAccumulatorPair.Plo -rm -f ./$(DEPDIR)/amiseOptimalBandwidth.Plo -rm -f ./$(DEPDIR)/bivariateChiSquare.Plo -rm -f ./$(DEPDIR)/buildInterpolatedHelpers.Plo -rm -f ./$(DEPDIR)/continuousDegreeTaper.Plo -rm -f ./$(DEPDIR)/convertAxis.Plo -rm -f ./$(DEPDIR)/correctDensityEstimateGHU.Plo -rm -f ./$(DEPDIR)/distributionReadError.Plo -rm -f ./$(DEPDIR)/distro1DStats.Plo -rm -f ./$(DEPDIR)/fitSbParameters.Plo -rm -f ./$(DEPDIR)/gaussianResponseMatrix.Plo -rm -f ./$(DEPDIR)/johnsonIntegral.Plo -rm -f ./$(DEPDIR)/johnsonTransform.Plo -rm -f ./$(DEPDIR)/likelihoodStatisticCumulants.Plo -rm -f ./$(DEPDIR)/logLikelihoodPeak.Plo -rm -f ./$(DEPDIR)/lorpeMise1D.Plo -rm -f ./$(DEPDIR)/make_UnbinnedGOFTest1D.Plo -rm -f ./$(DEPDIR)/multinomialCovariance1D.Plo -rm -f ./$(DEPDIR)/neymanPearsonWindow1D.Plo -rm -f ./$(DEPDIR)/saddlepointDistribution1D.Plo -rm -f ./$(DEPDIR)/scanMultivariateDensityAsWeight.Plo -rm -f ./$(DEPDIR)/scanSymmetricDensityAsWeight.Plo -rm -f ./$(DEPDIR)/scannedKSDistance.Plo -rm -f ./$(DEPDIR)/solveForLOrPEWeights.Plo -rm -f ./$(DEPDIR)/statUncertainties.Plo -rm -f ./$(DEPDIR)/volumeDensityFromBinnedRadial.Plo -rm -f Makefile maintainer-clean-am: distclean-am maintainer-clean-generic mostlyclean: mostlyclean-am mostlyclean-am: mostlyclean-compile mostlyclean-generic \ mostlyclean-libtool pdf: pdf-am pdf-am: ps: ps-am ps-am: python: python-am python-am: python-local uninstall-am: uninstall-includeHEADERS .MAKE: install-am install-strip .PHONY: CTAGS GTAGS TAGS all all-am am--depfiles check check-am clean \ clean-generic clean-libtool clean-noinstLTLIBRARIES \ cscopelist-am ctags ctags-am distclean distclean-compile \ distclean-generic distclean-libtool distclean-tags distdir dvi \ dvi-am html html-am info info-am install install-am \ install-data install-data-am install-dvi install-dvi-am \ install-exec install-exec-am install-html install-html-am \ install-includeHEADERS install-info install-info-am \ install-man install-pdf install-pdf-am install-ps \ install-ps-am install-strip installcheck installcheck-am \ installdirs maintainer-clean maintainer-clean-generic \ mostlyclean mostlyclean-compile mostlyclean-generic \ mostlyclean-libtool pdf pdf-am ps ps-am python-am python-local \ tags tags-am uninstall uninstall-am uninstall-includeHEADERS .PRECIOUS: Makefile # Tell versions [3.59,3.63) of GNU make to not export all variables. # Otherwise a system limit (for SysV at least) may be exceeded. .NOEXPORT: Index: trunk/npstat/stat/DensityOrthoPoly1D.cc =================================================================== --- trunk/npstat/stat/DensityOrthoPoly1D.cc (revision 860) +++ trunk/npstat/stat/DensityOrthoPoly1D.cc (revision 861) @@ -1,80 +1,79 @@ -#include #include #include #include "npstat/stat/DensityOrthoPoly1D.hh" #include "npstat/nm/FejerQuadrature.hh" #include "npstat/nm/RecurrenceCoeffs.hh" namespace npstat { DensityOrthoPoly1D::DensityOrthoPoly1D(const AbsDistribution1D& distro, - const unsigned maxDegree, + const unsigned i_maxDegree, const unsigned nIntegPoints, const OrthoPolyMethod m) - : distro_(distro.clone()), maxdeg_(maxDegree) + : distro_(distro.clone()), maxdeg_(i_maxDegree) { if (maxdeg_) { if (!nIntegPoints) throw std::invalid_argument( "In npstat::DensityOrthoPoly1D constructor: the number of " "integration points must be positive"); std::shared_ptr quad = FejerQuadratureSet::quad(nIntegPoints); assert(quad->npoints() == nIntegPoints); initialize(*quad, m); } else { // Just believe that the distro is correctly // normalized. We should not be asked for the // recurrence coefficients in the future. rCoeffs_.reserve(1); rCoeffs_.push_back(Private::Recurrence(0.0L, 1.0L)); } } void DensityOrthoPoly1D::initialize(const FejerQuadrature& quad, const OrthoPolyMethod m) { typedef std::pair MeasurePt; const unsigned nIntegPoints = quad.npoints(); const DensityFunctor1D weight(*distro_); const std::vector& measure = quad.weightedIntegrationPoints( weight, distro_->quantile(0.0), distro_->quantile(1.0)); assert(measure.size() == nIntegPoints); long double wsum = 0.0L; for (unsigned i=0; i DensityOrthoPoly1D::recurrenceCoeffs(const unsigned deg) const { const Private::Recurrence& r = rCoeffs_.at(deg); return std::pair(r.alpha, r.sqrbeta); } DensityOrthoPoly1D::DensityOrthoPoly1D(const DensityOrthoPoly1D& r) : distro_(r.distro_->clone()), rCoeffs_(r.rCoeffs_), maxdeg_(r.maxdeg_) { } DensityOrthoPoly1D& DensityOrthoPoly1D::operator=(const DensityOrthoPoly1D& r) { if (&r != this) { distro_ = CPP11_auto_ptr(r.distro_->clone()); rCoeffs_ = r.rCoeffs_; maxdeg_ = r.maxdeg_; } return *this; } } Index: trunk/config.log =================================================================== --- trunk/config.log (revision 860) +++ trunk/config.log (revision 861) @@ -1,1177 +1,1177 @@ This file contains any messages produced by compilers while running configure, to aid debugging if configure makes a mistake. It was created by npstat configure 5.9.0, which was generated by GNU Autoconf 2.71. Invocation command line was $ ./configure --disable-static --with-pic ## --------- ## ## Platform. ## ## --------- ## hostname = dawn uname -m = x86_64 uname -r = 5.15.0-52-generic uname -s = Linux uname -v = #58-Ubuntu SMP Thu Oct 13 08:03:55 UTC 2022 /usr/bin/uname -p = x86_64 /bin/uname -X = unknown /bin/arch = x86_64 /usr/bin/arch -k = unknown /usr/convex/getsysinfo = unknown /usr/bin/hostinfo = unknown /bin/machine = unknown /usr/bin/oslevel = unknown /bin/universe = unknown PATH: /home/igv/bin/ PATH: /home/igv/local/bin/ PATH: /usr/local/anaconda3/bin/ PATH: /usr/local/bin/ PATH: /usr/local/root/bin/ PATH: /usr/local/bin/ PATH: /bin/ PATH: /usr/bin/ PATH: /sbin/ PATH: /usr/sbin/ PATH: ./ ## ----------- ## ## Core tests. ## ## ----------- ## configure:2816: looking for aux files: compile ltmain.sh config.guess config.sub missing install-sh configure:2829: trying ./ configure:2858: ./compile found configure:2858: ./ltmain.sh found configure:2858: ./config.guess found configure:2858: ./config.sub found configure:2858: ./missing found configure:2840: ./install-sh found configure:2988: checking for a BSD-compatible install configure:3061: result: /bin/install -c configure:3072: checking whether build environment is sane configure:3127: result: yes configure:3286: checking for a race-free mkdir -p configure:3330: result: /bin/mkdir -p configure:3337: checking for gawk configure:3372: result: no configure:3337: checking for mawk configure:3358: found /bin/mawk configure:3369: result: mawk configure:3380: checking whether make sets $(MAKE) configure:3403: result: yes configure:3433: checking whether make supports nested variables configure:3451: result: yes configure:3650: checking for pkg-config configure:3673: found /bin/pkg-config configure:3685: result: /bin/pkg-config configure:3710: checking pkg-config is at least version 0.9.0 configure:3713: result: yes configure:3723: checking for fftw3 >= 3.1.2 geners >= 1.3.0 kstest >= 2.0.0 configure:3730: $PKG_CONFIG --exists --print-errors "fftw3 >= 3.1.2 geners >= 1.3.0 kstest >= 2.0.0" configure:3733: $? = 0 configure:3747: $PKG_CONFIG --exists --print-errors "fftw3 >= 3.1.2 geners >= 1.3.0 kstest >= 2.0.0" configure:3750: $? = 0 configure:3808: result: yes configure:3882: checking for g++ configure:3903: found /bin/g++ configure:3914: result: g++ configure:3941: checking for C++ compiler version configure:3950: g++ --version >&5 g++ (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Copyright (C) 2021 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. configure:3961: $? = 0 configure:3950: g++ -v >&5 Using built-in specs. COLLECT_GCC=g++ COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper OFFLOAD_TARGET_NAMES=nvptx-none:amdgcn-amdhsa OFFLOAD_TARGET_DEFAULT=1 Target: x86_64-linux-gnu Configured with: ../src/configure -v --with-pkgversion='Ubuntu 11.3.0-1ubuntu1~22.04' --with-bugurl=file:///usr/share/doc/gcc-11/README.Bugs --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --prefix=/usr --with-gcc-major-version-only --program-suffix=-11 --program-prefix=x86_64-linux-gnu- --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --enable-bootstrap --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-plugin --enable-default-pie --with-system-zlib --enable-libphobos-checking=release --with-target-system-zlib=auto --enable-objc-gc=auto --enable-multiarch --disable-werror --enable-cet --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-gcn/usr --without-cuda-driver --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu --with-build-config=bootstrap-lto-lean --enable-link-serialization=2 Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) ... rest of stderr output deleted ... configure:3961: $? = 0 configure:3950: g++ -V >&5 g++: error: unrecognized command-line option '-V' g++: fatal error: no input files compilation terminated. configure:3961: $? = 1 configure:3950: g++ -qversion >&5 g++: error: unrecognized command-line option '-qversion'; did you mean '--version'? g++: fatal error: no input files compilation terminated. configure:3961: $? = 1 configure:3981: checking whether the C++ compiler works configure:4003: g++ -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:4007: $? = 0 configure:4057: result: yes configure:4060: checking for C++ compiler default output file name configure:4062: result: a.out configure:4068: checking for suffix of executables configure:4075: g++ -o conftest -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:4079: $? = 0 configure:4102: result: configure:4124: checking whether we are cross compiling configure:4132: g++ -o conftest -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:4136: $? = 0 configure:4143: ./conftest configure:4147: $? = 0 configure:4162: result: no configure:4167: checking for suffix of object files configure:4190: g++ -c -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:4194: $? = 0 configure:4216: result: o configure:4220: checking whether the compiler supports GNU C++ configure:4240: g++ -c -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:4240: $? = 0 configure:4250: result: yes configure:4261: checking whether g++ accepts -g configure:4282: g++ -c -g conftest.cpp >&5 configure:4282: $? = 0 configure:4326: result: yes configure:4346: checking for g++ option to enable C++11 features configure:4361: g++ -c -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 conftest.cpp: In function 'int main(int, char**)': conftest.cpp:134:8: error: unused variable 'a1' [-Werror=unused-variable] 134 | auto a1 = 6538; | ^~ conftest.cpp:141:16: error: unused variable 'a4' [-Werror=unused-variable] 141 | decltype(a2) a4 = 34895.034; | ^~ conftest.cpp:145:9: error: unused variable 'sa' [-Werror=unused-variable] 145 | short sa[cxx11test::get_val()] = { 0 }; | ^~ conftest.cpp:149:23: error: unused variable 'il' [-Werror=unused-variable] 149 | cxx11test::testinit il = { 4323, 435234.23544 }; | ^~ conftest.cpp:170:8: error: unused variable 'a' [-Werror=unused-variable] 170 | auto a = sum(1); | ^ conftest.cpp:171:8: error: unused variable 'b' [-Werror=unused-variable] 171 | auto b = sum(1, 2); | ^ conftest.cpp:172:8: error: unused variable 'c' [-Werror=unused-variable] 172 | auto c = sum(1.0, 2.0, 3.0); | ^ conftest.cpp:177:25: error: empty parentheses were disambiguated as a function declaration [-Werror=vexing-parse] 177 | cxx11test::delegate d2(); | ^~ conftest.cpp:177:25: note: remove parentheses to default-initialize a variable 177 | cxx11test::delegate d2(); | ^~ | -- conftest.cpp:177:25: note: or replace parentheses with braces to value-initialize a variable conftest.cpp:186:9: error: unused variable 'c' [-Werror=unused-variable] 186 | char *c = nullptr; | ^ conftest.cpp:194:15: error: unused variable 'utf8' [-Werror=unused-variable] 194 | char const *utf8 = u8"UTF-8 string \u2500"; | ^~~~ conftest.cpp:195:19: error: unused variable 'utf16' [-Werror=unused-variable] 195 | char16_t const *utf16 = u"UTF-8 string \u2500"; | ^~~~~ conftest.cpp:196:19: error: unused variable 'utf32' [-Werror=unused-variable] 196 | char32_t const *utf32 = U"UTF-32 string \u2500"; | ^~~~~ cc1plus: all warnings being treated as errors configure:4361: $? = 1 configure: failed program was: | /* confdefs.h */ | #define PACKAGE_NAME "npstat" | #define PACKAGE_TARNAME "npstat" | #define PACKAGE_VERSION "5.9.0" | #define PACKAGE_STRING "npstat 5.9.0" | #define PACKAGE_BUGREPORT "" | #define PACKAGE_URL "" | #define PACKAGE "npstat" | #define VERSION "5.9.0" | /* end confdefs.h. */ | | // Does the compiler advertise C++98 conformance? | #if !defined __cplusplus || __cplusplus < 199711L | # error "Compiler does not advertise C++98 conformance" | #endif | | // These inclusions are to reject old compilers that | // lack the unsuffixed header files. | #include | #include | | // and are *not* freestanding headers in C++98. | extern void assert (int); | namespace std { | extern int strcmp (const char *, const char *); | } | | // Namespaces, exceptions, and templates were all added after "C++ 2.0". | using std::exception; | using std::strcmp; | | namespace { | | void test_exception_syntax() | { | try { | throw "test"; | } catch (const char *s) { | // Extra parentheses suppress a warning when building autoconf itself, | // due to lint rules shared with more typical C programs. | assert (!(strcmp) (s, "test")); | } | } | | template struct test_template | { | T const val; | explicit test_template(T t) : val(t) {} | template T add(U u) { return static_cast(u) + val; } | }; | | } // anonymous namespace | | | // Does the compiler advertise C++ 2011 conformance? | #if !defined __cplusplus || __cplusplus < 201103L | # error "Compiler does not advertise C++11 conformance" | #endif | | namespace cxx11test | { | constexpr int get_val() { return 20; } | | struct testinit | { | int i; | double d; | }; | | class delegate | { | public: | delegate(int n) : n(n) {} | delegate(): delegate(2354) {} | | virtual int getval() { return this->n; }; | protected: | int n; | }; | | class overridden : public delegate | { | public: | overridden(int n): delegate(n) {} | virtual int getval() override final { return this->n * 2; } | }; | | class nocopy | { | public: | nocopy(int i): i(i) {} | nocopy() = default; | nocopy(const nocopy&) = delete; | nocopy & operator=(const nocopy&) = delete; | private: | int i; | }; | | // for testing lambda expressions | template Ret eval(Fn f, Ret v) | { | return f(v); | } | | // for testing variadic templates and trailing return types | template auto sum(V first) -> V | { | return first; | } | template auto sum(V first, Args... rest) -> V | { | return first + sum(rest...); | } | } | | | int | main (int argc, char **argv) | { | int ok = 0; | | assert (argc); | assert (! argv[0]); | { | test_exception_syntax (); | test_template tt (2.0); | assert (tt.add (4) == 6.0); | assert (true && !false); | } | | | { | // Test auto and decltype | auto a1 = 6538; | auto a2 = 48573953.4; | auto a3 = "String literal"; | | int total = 0; | for (auto i = a3; *i; ++i) { total += *i; } | | decltype(a2) a4 = 34895.034; | } | { | // Test constexpr | short sa[cxx11test::get_val()] = { 0 }; | } | { | // Test initializer lists | cxx11test::testinit il = { 4323, 435234.23544 }; | } | { | // Test range-based for | int array[] = {9, 7, 13, 15, 4, 18, 12, 10, 5, 3, | 14, 19, 17, 8, 6, 20, 16, 2, 11, 1}; | for (auto &x : array) { x += 23; } | } | { | // Test lambda expressions | using cxx11test::eval; | assert (eval ([](int x) { return x*2; }, 21) == 42); | double d = 2.0; | assert (eval ([&](double x) { return d += x; }, 3.0) == 5.0); | assert (d == 5.0); | assert (eval ([=](double x) mutable { return d += x; }, 4.0) == 9.0); | assert (d == 5.0); | } | { | // Test use of variadic templates | using cxx11test::sum; | auto a = sum(1); | auto b = sum(1, 2); | auto c = sum(1.0, 2.0, 3.0); | } | { | // Test constructor delegation | cxx11test::delegate d1; | cxx11test::delegate d2(); | cxx11test::delegate d3(45); | } | { | // Test override and final | cxx11test::overridden o1(55464); | } | { | // Test nullptr | char *c = nullptr; | } | { | // Test template brackets | test_template<::test_template> v(test_template(12)); | } | { | // Unicode literals | char const *utf8 = u8"UTF-8 string \u2500"; | char16_t const *utf16 = u"UTF-8 string \u2500"; | char32_t const *utf32 = U"UTF-32 string \u2500"; | } | | return ok; | } | configure:4379: result: none needed configure:4446: checking whether make supports the include directive configure:4461: make -f confmf.GNU && cat confinc.out this is the am__doit target configure:4464: $? = 0 configure:4483: result: yes (GNU style) configure:4509: checking dependency style of g++ configure:4621: result: gcc3 configure:4695: checking for g77 configure:4716: found /home/igv/bin/g77 configure:4727: result: g77 configure:4753: checking for Fortran 77 compiler version configure:4762: g77 --version >&5 GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Copyright (C) 2021 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. configure:4773: $? = 0 configure:4762: g77 -v >&5 Using built-in specs. COLLECT_GCC=g77 COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper OFFLOAD_TARGET_NAMES=nvptx-none:amdgcn-amdhsa OFFLOAD_TARGET_DEFAULT=1 Target: x86_64-linux-gnu Configured with: ../src/configure -v --with-pkgversion='Ubuntu 11.3.0-1ubuntu1~22.04' --with-bugurl=file:///usr/share/doc/gcc-11/README.Bugs --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --prefix=/usr --with-gcc-major-version-only --program-suffix=-11 --program-prefix=x86_64-linux-gnu- --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --enable-bootstrap --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-plugin --enable-default-pie --with-system-zlib --enable-libphobos-checking=release --with-target-system-zlib=auto --enable-objc-gc=auto --enable-multiarch --disable-werror --enable-cet --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-gcn/usr --without-cuda-driver --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu --with-build-config=bootstrap-lto-lean --enable-link-serialization=2 Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) ... rest of stderr output deleted ... configure:4773: $? = 0 configure:4762: g77 -V >&5 g77: error: unrecognized command-line option '-V' g77: fatal error: no input files compilation terminated. configure:4773: $? = 1 configure:4762: g77 -qversion >&5 g77: error: unrecognized command-line option '-qversion'; did you mean '--version'? g77: fatal error: no input files compilation terminated. configure:4773: $? = 1 configure:4782: checking whether the compiler supports GNU Fortran 77 configure:4796: g77 -c conftest.F >&5 configure:4796: $? = 0 configure:4806: result: yes configure:4814: checking whether g77 accepts -g configure:4826: g77 -c -g conftest.f >&5 configure:4826: $? = 0 configure:4835: result: yes configure:4870: checking build system type configure:4885: result: x86_64-pc-linux-gnu configure:4905: checking host system type configure:4919: result: x86_64-pc-linux-gnu configure:4944: checking how to get verbose linking output from g77 configure:4955: g77 -c -g -O2 conftest.f >&5 configure:4955: $? = 0 configure:4974: g77 -o conftest -g -O2 -v conftest.f Using built-in specs. Target: x86_64-linux-gnu Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) - /usr/lib/gcc/x86_64-linux-gnu/11/f951 conftest.f -ffixed-form -quiet -dumpbase conftest.f -dumpbase-ext .f -mtune=generic -march=x86-64 -g -O2 -version -fintrinsic-modules-path /usr/lib/gcc/x86_64-linux-gnu/11/finclude -fpre-include=/usr/include/finclude/math-vector-fortran.h -o /tmp/ccf7WtuT.s + /usr/lib/gcc/x86_64-linux-gnu/11/f951 conftest.f -ffixed-form -quiet -dumpbase conftest.f -dumpbase-ext .f -mtune=generic -march=x86-64 -g -O2 -version -fintrinsic-modules-path /usr/lib/gcc/x86_64-linux-gnu/11/finclude -fpre-include=/usr/include/finclude/math-vector-fortran.h -o /tmp/ccKBVoUm.s GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04) version 11.3.0 (x86_64-linux-gnu) compiled by GNU C version 11.3.0, GMP version 6.2.1, MPFR version 4.1.0, MPC version 1.2.1, isl version isl-0.24-GMP GGC heuristics: --param ggc-min-expand=100 --param ggc-min-heapsize=131072 GNU Fortran2008 (Ubuntu 11.3.0-1ubuntu1~22.04) version 11.3.0 (x86_64-linux-gnu) compiled by GNU C version 11.3.0, GMP version 6.2.1, MPFR version 4.1.0, MPC version 1.2.1, isl version isl-0.24-GMP GGC heuristics: --param ggc-min-expand=100 --param ggc-min-heapsize=131072 - as -v --gdwarf-5 --64 -o /tmp/ccv1ChHi.o /tmp/ccf7WtuT.s + as -v --gdwarf-5 --64 -o /tmp/cckJk8d4.o /tmp/ccKBVoUm.s GNU assembler version 2.38 (x86_64-linux-gnu) using BFD version (GNU Binutils for Ubuntu) 2.38 Reading specs from /usr/lib/gcc/x86_64-linux-gnu/11/libgfortran.spec rename spec lib to liborig - /usr/lib/gcc/x86_64-linux-gnu/11/collect2 -plugin /usr/lib/gcc/x86_64-linux-gnu/11/liblto_plugin.so -plugin-opt=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper -plugin-opt=-fresolution=/tmp/ccv0i0mu.res -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lquadmath -plugin-opt=-pass-through=-lm -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lc -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc --build-id --eh-frame-hdr -m elf_x86_64 --hash-style=gnu --as-needed -dynamic-linker /lib64/ld-linux-x86-64.so.2 -pie -z now -z relro -o conftest /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/Scrt1.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/11/crtbeginS.o -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. /tmp/ccv1ChHi.o -lgfortran -lm -lgcc_s -lgcc -lquadmath -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /usr/lib/gcc/x86_64-linux-gnu/11/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crtn.o + /usr/lib/gcc/x86_64-linux-gnu/11/collect2 -plugin /usr/lib/gcc/x86_64-linux-gnu/11/liblto_plugin.so -plugin-opt=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper -plugin-opt=-fresolution=/tmp/ccdLGz2W.res -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lquadmath -plugin-opt=-pass-through=-lm -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lc -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc --build-id --eh-frame-hdr -m elf_x86_64 --hash-style=gnu --as-needed -dynamic-linker /lib64/ld-linux-x86-64.so.2 -pie -z now -z relro -o conftest /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/Scrt1.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/11/crtbeginS.o -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. /tmp/cckJk8d4.o -lgfortran -lm -lgcc_s -lgcc -lquadmath -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /usr/lib/gcc/x86_64-linux-gnu/11/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crtn.o configure:5057: result: -v configure:5059: checking for Fortran 77 libraries of g77 configure:5083: g77 -o conftest -g -O2 -v conftest.f Using built-in specs. Target: x86_64-linux-gnu Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) - /usr/lib/gcc/x86_64-linux-gnu/11/f951 conftest.f -ffixed-form -quiet -dumpbase conftest.f -dumpbase-ext .f -mtune=generic -march=x86-64 -g -O2 -version -fintrinsic-modules-path /usr/lib/gcc/x86_64-linux-gnu/11/finclude -fpre-include=/usr/include/finclude/math-vector-fortran.h -o /tmp/ccdGwaHu.s + /usr/lib/gcc/x86_64-linux-gnu/11/f951 conftest.f -ffixed-form -quiet -dumpbase conftest.f -dumpbase-ext .f -mtune=generic -march=x86-64 -g -O2 -version -fintrinsic-modules-path /usr/lib/gcc/x86_64-linux-gnu/11/finclude -fpre-include=/usr/include/finclude/math-vector-fortran.h -o /tmp/ccomIoXZ.s GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04) version 11.3.0 (x86_64-linux-gnu) compiled by GNU C version 11.3.0, GMP version 6.2.1, MPFR version 4.1.0, MPC version 1.2.1, isl version isl-0.24-GMP GGC heuristics: --param ggc-min-expand=100 --param ggc-min-heapsize=131072 GNU Fortran2008 (Ubuntu 11.3.0-1ubuntu1~22.04) version 11.3.0 (x86_64-linux-gnu) compiled by GNU C version 11.3.0, GMP version 6.2.1, MPFR version 4.1.0, MPC version 1.2.1, isl version isl-0.24-GMP GGC heuristics: --param ggc-min-expand=100 --param ggc-min-heapsize=131072 - as -v --gdwarf-5 --64 -o /tmp/ccUWTSx0.o /tmp/ccdGwaHu.s + as -v --gdwarf-5 --64 -o /tmp/ccdjw95d.o /tmp/ccomIoXZ.s GNU assembler version 2.38 (x86_64-linux-gnu) using BFD version (GNU Binutils for Ubuntu) 2.38 Reading specs from /usr/lib/gcc/x86_64-linux-gnu/11/libgfortran.spec rename spec lib to liborig - /usr/lib/gcc/x86_64-linux-gnu/11/collect2 -plugin /usr/lib/gcc/x86_64-linux-gnu/11/liblto_plugin.so -plugin-opt=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper -plugin-opt=-fresolution=/tmp/ccdRpEUF.res -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lquadmath -plugin-opt=-pass-through=-lm -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lc -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc --build-id --eh-frame-hdr -m elf_x86_64 --hash-style=gnu --as-needed -dynamic-linker /lib64/ld-linux-x86-64.so.2 -pie -z now -z relro -o conftest /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/Scrt1.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/11/crtbeginS.o -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. /tmp/ccUWTSx0.o -lgfortran -lm -lgcc_s -lgcc -lquadmath -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /usr/lib/gcc/x86_64-linux-gnu/11/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crtn.o + /usr/lib/gcc/x86_64-linux-gnu/11/collect2 -plugin /usr/lib/gcc/x86_64-linux-gnu/11/liblto_plugin.so -plugin-opt=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper -plugin-opt=-fresolution=/tmp/ccEyRoVW.res -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lquadmath -plugin-opt=-pass-through=-lm -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc -plugin-opt=-pass-through=-lc -plugin-opt=-pass-through=-lgcc_s -plugin-opt=-pass-through=-lgcc --build-id --eh-frame-hdr -m elf_x86_64 --hash-style=gnu --as-needed -dynamic-linker /lib64/ld-linux-x86-64.so.2 -pie -z now -z relro -o conftest /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/Scrt1.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/11/crtbeginS.o -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. /tmp/ccdjw95d.o -lgfortran -lm -lgcc_s -lgcc -lquadmath -lm -lgcc_s -lgcc -lc -lgcc_s -lgcc /usr/lib/gcc/x86_64-linux-gnu/11/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu/crtn.o configure:5299: result: -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. -lgfortran -lm -lquadmath configure:5362: checking how to print strings configure:5389: result: printf configure:5472: checking for gcc configure:5493: found /bin/gcc configure:5504: result: gcc configure:5857: checking for C compiler version configure:5866: gcc --version >&5 gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Copyright (C) 2021 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. configure:5877: $? = 0 configure:5866: gcc -v >&5 Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/11/lto-wrapper OFFLOAD_TARGET_NAMES=nvptx-none:amdgcn-amdhsa OFFLOAD_TARGET_DEFAULT=1 Target: x86_64-linux-gnu Configured with: ../src/configure -v --with-pkgversion='Ubuntu 11.3.0-1ubuntu1~22.04' --with-bugurl=file:///usr/share/doc/gcc-11/README.Bugs --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --prefix=/usr --with-gcc-major-version-only --program-suffix=-11 --program-prefix=x86_64-linux-gnu- --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --enable-bootstrap --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-plugin --enable-default-pie --with-system-zlib --enable-libphobos-checking=release --with-target-system-zlib=auto --enable-objc-gc=auto --enable-multiarch --disable-werror --enable-cet --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-gcn/usr --without-cuda-driver --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu --with-build-config=bootstrap-lto-lean --enable-link-serialization=2 Thread model: posix Supported LTO compression algorithms: zlib zstd gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04) ... rest of stderr output deleted ... configure:5877: $? = 0 configure:5866: gcc -V >&5 gcc: error: unrecognized command-line option '-V' gcc: fatal error: no input files compilation terminated. configure:5877: $? = 1 configure:5866: gcc -qversion >&5 gcc: error: unrecognized command-line option '-qversion'; did you mean '--version'? gcc: fatal error: no input files compilation terminated. configure:5877: $? = 1 configure:5866: gcc -version >&5 gcc: error: unrecognized command-line option '-version' gcc: fatal error: no input files compilation terminated. configure:5877: $? = 1 configure:5881: checking whether the compiler supports GNU C configure:5901: gcc -c conftest.c >&5 configure:5901: $? = 0 configure:5911: result: yes configure:5922: checking whether gcc accepts -g configure:5943: gcc -c -g conftest.c >&5 configure:5943: $? = 0 configure:5987: result: yes configure:6007: checking for gcc option to enable C11 features configure:6022: gcc -c -g -O2 conftest.c >&5 configure:6022: $? = 0 configure:6040: result: none needed configure:6156: checking whether gcc understands -c and -o together configure:6179: gcc -c conftest.c -o conftest2.o configure:6182: $? = 0 configure:6179: gcc -c conftest.c -o conftest2.o configure:6182: $? = 0 configure:6194: result: yes configure:6213: checking dependency style of gcc configure:6325: result: gcc3 configure:6340: checking for a sed that does not truncate output configure:6410: result: /bin/sed configure:6428: checking for grep that handles long lines and -e configure:6492: result: /bin/grep configure:6497: checking for egrep configure:6565: result: /bin/grep -E configure:6570: checking for fgrep configure:6638: result: /bin/grep -F configure:6674: checking for ld used by gcc configure:6742: result: /bin/ld configure:6749: checking if the linker (/bin/ld) is GNU ld configure:6765: result: yes configure:6777: checking for BSD- or MS-compatible name lister (nm) configure:6832: result: /bin/nm -B configure:6972: checking the name lister (/bin/nm -B) interface configure:6980: gcc -c -g -O2 conftest.c >&5 configure:6983: /bin/nm -B "conftest.o" configure:6986: output 0000000000000000 B some_variable configure:6993: result: BSD nm configure:6996: checking whether ln -s works configure:7000: result: yes configure:7008: checking the maximum length of command line arguments configure:7140: result: 1572864 configure:7188: checking how to convert x86_64-pc-linux-gnu file names to x86_64-pc-linux-gnu format configure:7229: result: func_convert_file_noop configure:7236: checking how to convert x86_64-pc-linux-gnu file names to toolchain format configure:7257: result: func_convert_file_noop configure:7264: checking for /bin/ld option to reload object files configure:7272: result: -r configure:7351: checking for objdump configure:7372: found /bin/objdump configure:7383: result: objdump configure:7415: checking how to recognize dependent libraries configure:7616: result: pass_all configure:7706: checking for dlltool configure:7741: result: no configure:7771: checking how to associate runtime and link libraries configure:7799: result: printf %s\n configure:7865: checking for ar configure:7886: found /bin/ar configure:7897: result: ar configure:7934: checking for archiver @FILE support configure:7952: gcc -c -g -O2 conftest.c >&5 configure:7952: $? = 0 configure:7956: ar cr libconftest.a @conftest.lst >&5 configure:7959: $? = 0 configure:7964: ar cr libconftest.a @conftest.lst >&5 ar: conftest.o: No such file or directory configure:7967: $? = 1 configure:7979: result: @ configure:8042: checking for strip configure:8063: found /bin/strip configure:8074: result: strip configure:8151: checking for ranlib configure:8172: found /bin/ranlib configure:8183: result: ranlib configure:8285: checking command to parse /bin/nm -B output from gcc object configure:8439: gcc -c -g -O2 conftest.c >&5 configure:8442: $? = 0 configure:8446: /bin/nm -B conftest.o | sed -n -e 's/^.*[ ]\([ABCDGIRSTW][ABCDGIRSTW]*\)[ ][ ]*\([_A-Za-z][_A-Za-z0-9]*\)$/\1 \2 \2/p' | sed '/ __gnu_lto/d' > conftest.nm configure:8512: gcc -o conftest -g -O2 conftest.c conftstm.o >&5 configure:8515: $? = 0 configure:8553: result: ok configure:8600: checking for sysroot configure:8631: result: no configure:8638: checking for a working dd configure:8682: result: /bin/dd configure:8686: checking how to truncate binary pipes configure:8702: result: /bin/dd bs=4096 count=1 configure:8839: gcc -c -g -O2 conftest.c >&5 configure:8842: $? = 0 configure:9039: checking for mt configure:9060: found /bin/mt configure:9071: result: mt configure:9094: checking if mt is a manifest tool configure:9101: mt '-?' configure:9109: result: no configure:9839: checking for stdio.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for stdlib.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for string.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for inttypes.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for stdint.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for strings.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for sys/stat.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for sys/types.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9839: checking for unistd.h configure:9839: gcc -c -g -O2 conftest.c >&5 configure:9839: $? = 0 configure:9839: result: yes configure:9864: checking for dlfcn.h configure:9864: gcc -c -g -O2 conftest.c >&5 configure:9864: $? = 0 configure:9864: result: yes configure:10134: checking for objdir configure:10150: result: .libs configure:10414: checking if gcc supports -fno-rtti -fno-exceptions configure:10433: gcc -c -g -O2 -fno-rtti -fno-exceptions conftest.c >&5 cc1: warning: command-line option '-fno-rtti' is valid for C++/D/ObjC++ but not for C configure:10437: $? = 0 configure:10450: result: no configure:10814: checking for gcc option to produce PIC configure:10822: result: -fPIC -DPIC configure:10830: checking if gcc PIC flag -fPIC -DPIC works configure:10849: gcc -c -g -O2 -fPIC -DPIC -DPIC conftest.c >&5 configure:10853: $? = 0 configure:10866: result: yes configure:10895: checking if gcc static flag -static works configure:10924: result: yes configure:10939: checking if gcc supports -c -o file.o configure:10961: gcc -c -g -O2 -o out/conftest2.o conftest.c >&5 configure:10965: $? = 0 configure:10987: result: yes configure:10995: checking if gcc supports -c -o file.o configure:11043: result: yes configure:11076: checking whether the gcc linker (/bin/ld -m elf_x86_64) supports shared libraries configure:12346: result: yes configure:12383: checking whether -lc should be explicitly linked in configure:12392: gcc -c -g -O2 conftest.c >&5 configure:12395: $? = 0 configure:12410: gcc -shared -fPIC -DPIC conftest.o -v -Wl,-soname -Wl,conftest -o conftest 2\>\&1 \| /bin/grep -lc \>/dev/null 2\>\&1 configure:12413: $? = 0 configure:12427: result: no configure:12587: checking dynamic linker characteristics configure:13169: gcc -o conftest -g -O2 -Wl,-rpath -Wl,/foo conftest.c >&5 configure:13169: $? = 0 configure:13420: result: GNU/Linux ld.so configure:13542: checking how to hardcode library paths into programs configure:13567: result: immediate configure:14119: checking whether stripping libraries is possible configure:14124: result: yes configure:14159: checking if libtool supports shared libraries configure:14161: result: yes configure:14164: checking whether to build shared libraries configure:14189: result: yes configure:14192: checking whether to build static libraries configure:14196: result: no configure:14219: checking how to run the C++ preprocessor configure:14241: g++ -E conftest.cpp configure:14241: $? = 0 configure:14256: g++ -E conftest.cpp conftest.cpp:23:10: fatal error: ac_nonexistent.h: No such file or directory 23 | #include | ^~~~~~~~~~~~~~~~~~ compilation terminated. configure:14256: $? = 1 configure: failed program was: | /* confdefs.h */ | #define PACKAGE_NAME "npstat" | #define PACKAGE_TARNAME "npstat" | #define PACKAGE_VERSION "5.9.0" | #define PACKAGE_STRING "npstat 5.9.0" | #define PACKAGE_BUGREPORT "" | #define PACKAGE_URL "" | #define PACKAGE "npstat" | #define VERSION "5.9.0" | #define HAVE_STDIO_H 1 | #define HAVE_STDLIB_H 1 | #define HAVE_STRING_H 1 | #define HAVE_INTTYPES_H 1 | #define HAVE_STDINT_H 1 | #define HAVE_STRINGS_H 1 | #define HAVE_SYS_STAT_H 1 | #define HAVE_SYS_TYPES_H 1 | #define HAVE_UNISTD_H 1 | #define STDC_HEADERS 1 | #define HAVE_DLFCN_H 1 | #define LT_OBJDIR ".libs/" | /* end confdefs.h. */ | #include configure:14283: result: g++ -E configure:14297: g++ -E conftest.cpp configure:14297: $? = 0 configure:14312: g++ -E conftest.cpp conftest.cpp:23:10: fatal error: ac_nonexistent.h: No such file or directory 23 | #include | ^~~~~~~~~~~~~~~~~~ compilation terminated. configure:14312: $? = 1 configure: failed program was: | /* confdefs.h */ | #define PACKAGE_NAME "npstat" | #define PACKAGE_TARNAME "npstat" | #define PACKAGE_VERSION "5.9.0" | #define PACKAGE_STRING "npstat 5.9.0" | #define PACKAGE_BUGREPORT "" | #define PACKAGE_URL "" | #define PACKAGE "npstat" | #define VERSION "5.9.0" | #define HAVE_STDIO_H 1 | #define HAVE_STDLIB_H 1 | #define HAVE_STRING_H 1 | #define HAVE_INTTYPES_H 1 | #define HAVE_STDINT_H 1 | #define HAVE_STRINGS_H 1 | #define HAVE_SYS_STAT_H 1 | #define HAVE_SYS_TYPES_H 1 | #define HAVE_UNISTD_H 1 | #define STDC_HEADERS 1 | #define HAVE_DLFCN_H 1 | #define LT_OBJDIR ".libs/" | /* end confdefs.h. */ | #include configure:14477: checking for ld used by g++ configure:14545: result: /bin/ld -m elf_x86_64 configure:14552: checking if the linker (/bin/ld -m elf_x86_64) is GNU ld configure:14568: result: yes configure:14623: checking whether the g++ linker (/bin/ld -m elf_x86_64) supports shared libraries configure:15700: result: yes configure:15736: g++ -c -std=c++11 -O3 -Wall -W -Werror conftest.cpp >&5 configure:15739: $? = 0 configure:16220: checking for g++ option to produce PIC configure:16228: result: -fPIC -DPIC configure:16236: checking if g++ PIC flag -fPIC -DPIC works configure:16255: g++ -c -std=c++11 -O3 -Wall -W -Werror -fPIC -DPIC -DPIC conftest.cpp >&5 configure:16259: $? = 0 configure:16272: result: yes configure:16295: checking if g++ static flag -static works configure:16324: result: yes configure:16336: checking if g++ supports -c -o file.o configure:16358: g++ -c -std=c++11 -O3 -Wall -W -Werror -o out/conftest2.o conftest.cpp >&5 configure:16362: $? = 0 configure:16384: result: yes configure:16389: checking if g++ supports -c -o file.o configure:16437: result: yes configure:16467: checking whether the g++ linker (/bin/ld -m elf_x86_64) supports shared libraries configure:16510: result: yes configure:16652: checking dynamic linker characteristics configure:17412: result: GNU/Linux ld.so configure:17477: checking how to hardcode library paths into programs configure:17502: result: immediate configure:17643: checking if libtool supports shared libraries configure:17645: result: yes configure:17648: checking whether to build shared libraries configure:17672: result: yes configure:17675: checking whether to build static libraries configure:17679: result: no configure:18037: checking for g77 option to produce PIC configure:18045: result: -fPIC configure:18053: checking if g77 PIC flag -fPIC works configure:18072: g77 -c -g -O2 -fPIC conftest.f >&5 configure:18076: $? = 0 configure:18089: result: yes configure:18112: checking if g77 static flag -static works configure:18141: result: yes configure:18153: checking if g77 supports -c -o file.o configure:18175: g77 -c -g -O2 -o out/conftest2.o conftest.f >&5 configure:18179: $? = 0 configure:18201: result: yes configure:18206: checking if g77 supports -c -o file.o configure:18254: result: yes configure:18284: checking whether the g77 linker (/bin/ld -m elf_x86_64) supports shared libraries configure:19503: result: yes configure:19645: checking dynamic linker characteristics configure:20399: result: GNU/Linux ld.so configure:20464: checking how to hardcode library paths into programs configure:20489: result: immediate configure:20681: checking that generated files are newer than configure configure:20687: result: done configure:20714: creating ./config.status ## ---------------------- ## ## Running config.status. ## ## ---------------------- ## This file was extended by npstat config.status 5.9.0, which was generated by GNU Autoconf 2.71. Invocation command line was CONFIG_FILES = CONFIG_HEADERS = CONFIG_LINKS = CONFIG_COMMANDS = $ ./config.status on dawn config.status:1138: creating Makefile config.status:1138: creating npstat/nm/Makefile config.status:1138: creating npstat/rng/Makefile config.status:1138: creating npstat/stat/Makefile config.status:1138: creating npstat/wrap/Makefile config.status:1138: creating npstat/interfaces/Makefile config.status:1138: creating npstat/emsunfold/Makefile config.status:1138: creating npstat/Makefile config.status:1138: creating examples/C++/Makefile config.status:1138: creating npstat/swig/Makefile config.status:1138: creating npstat.pc config.status:1310: executing depfiles commands config.status:1387: cd npstat/nm && sed -e '/# am--include-marker/d' Makefile | make -f - am--depfiles config.status:1392: $? = 0 config.status:1387: cd npstat/rng && sed -e '/# am--include-marker/d' Makefile | make -f - am--depfiles config.status:1392: $? = 0 config.status:1387: cd npstat/stat && sed -e '/# am--include-marker/d' Makefile | make -f - am--depfiles config.status:1392: $? = 0 config.status:1387: cd examples/C++ && sed -e '/# am--include-marker/d' Makefile | make -f - am--depfiles config.status:1392: $? = 0 config.status:1387: cd npstat/swig && sed -e '/# am--include-marker/d' Makefile | make -f - am--depfiles make: Nothing to be done for 'am--depfiles'. config.status:1392: $? = 0 config.status:1310: executing libtool commands ## ---------------- ## ## Cache variables. ## ## ---------------- ## ac_cv_build=x86_64-pc-linux-gnu ac_cv_c_compiler_gnu=yes ac_cv_cxx_compiler_gnu=yes ac_cv_env_CCC_set= ac_cv_env_CCC_value= ac_cv_env_CC_set= ac_cv_env_CC_value= ac_cv_env_CFLAGS_set= ac_cv_env_CFLAGS_value= ac_cv_env_CPPFLAGS_set= ac_cv_env_CPPFLAGS_value= ac_cv_env_CXXCPP_set= ac_cv_env_CXXCPP_value= ac_cv_env_CXXFLAGS_set=set ac_cv_env_CXXFLAGS_value='-std=c++11 -O3 -Wall -W -Werror' ac_cv_env_CXX_set= ac_cv_env_CXX_value= ac_cv_env_DEPS_CFLAGS_set= ac_cv_env_DEPS_CFLAGS_value= ac_cv_env_DEPS_LIBS_set= ac_cv_env_DEPS_LIBS_value= ac_cv_env_F77_set= ac_cv_env_F77_value= ac_cv_env_FFLAGS_set= ac_cv_env_FFLAGS_value= ac_cv_env_LDFLAGS_set= ac_cv_env_LDFLAGS_value= ac_cv_env_LIBS_set= ac_cv_env_LIBS_value= ac_cv_env_LT_SYS_LIBRARY_PATH_set= ac_cv_env_LT_SYS_LIBRARY_PATH_value= ac_cv_env_PKG_CONFIG_LIBDIR_set= ac_cv_env_PKG_CONFIG_LIBDIR_value= ac_cv_env_PKG_CONFIG_PATH_set=set ac_cv_env_PKG_CONFIG_PATH_value=/usr/local/lib/pkgconfig ac_cv_env_PKG_CONFIG_set= ac_cv_env_PKG_CONFIG_value= ac_cv_env_build_alias_set= ac_cv_env_build_alias_value= ac_cv_env_host_alias_set= ac_cv_env_host_alias_value= ac_cv_env_target_alias_set= ac_cv_env_target_alias_value= ac_cv_f77_compiler_gnu=yes ac_cv_f77_libs=' -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. -lgfortran -lm -lquadmath' ac_cv_header_dlfcn_h=yes ac_cv_header_inttypes_h=yes ac_cv_header_stdint_h=yes ac_cv_header_stdio_h=yes ac_cv_header_stdlib_h=yes ac_cv_header_string_h=yes ac_cv_header_strings_h=yes ac_cv_header_sys_stat_h=yes ac_cv_header_sys_types_h=yes ac_cv_header_unistd_h=yes ac_cv_host=x86_64-pc-linux-gnu ac_cv_objext=o ac_cv_path_EGREP='/bin/grep -E' ac_cv_path_FGREP='/bin/grep -F' ac_cv_path_GREP=/bin/grep ac_cv_path_SED=/bin/sed ac_cv_path_ac_pt_PKG_CONFIG=/bin/pkg-config ac_cv_path_install='/bin/install -c' ac_cv_path_lt_DD=/bin/dd ac_cv_path_mkdir=/bin/mkdir ac_cv_prog_AWK=mawk ac_cv_prog_CXXCPP='g++ -E' ac_cv_prog_ac_ct_AR=ar ac_cv_prog_ac_ct_CC=gcc ac_cv_prog_ac_ct_CXX=g++ ac_cv_prog_ac_ct_F77=g77 ac_cv_prog_ac_ct_MANIFEST_TOOL=mt ac_cv_prog_ac_ct_OBJDUMP=objdump ac_cv_prog_ac_ct_RANLIB=ranlib ac_cv_prog_ac_ct_STRIP=strip ac_cv_prog_cc_c11= ac_cv_prog_cc_g=yes ac_cv_prog_cc_stdc= ac_cv_prog_cxx_11=no ac_cv_prog_cxx_g=yes ac_cv_prog_cxx_stdcxx= ac_cv_prog_f77_g=yes ac_cv_prog_f77_v=-v ac_cv_prog_make_make_set=yes am_cv_CC_dependencies_compiler_type=gcc3 am_cv_CXX_dependencies_compiler_type=gcc3 am_cv_make_support_nested_variables=yes am_cv_prog_cc_c_o=yes lt_cv_ar_at_file=@ lt_cv_archive_cmds_need_lc=no lt_cv_deplibs_check_method=pass_all lt_cv_file_magic_cmd='$MAGIC_CMD' lt_cv_file_magic_test_file= lt_cv_ld_reload_flag=-r lt_cv_nm_interface='BSD nm' lt_cv_objdir=.libs lt_cv_path_LD=/bin/ld lt_cv_path_LDCXX='/bin/ld -m elf_x86_64' lt_cv_path_NM='/bin/nm -B' lt_cv_path_mainfest_tool=no lt_cv_prog_compiler_c_o=yes lt_cv_prog_compiler_c_o_CXX=yes lt_cv_prog_compiler_c_o_F77=yes lt_cv_prog_compiler_pic='-fPIC -DPIC' lt_cv_prog_compiler_pic_CXX='-fPIC -DPIC' lt_cv_prog_compiler_pic_F77=-fPIC lt_cv_prog_compiler_pic_works=yes lt_cv_prog_compiler_pic_works_CXX=yes lt_cv_prog_compiler_pic_works_F77=yes lt_cv_prog_compiler_rtti_exceptions=no lt_cv_prog_compiler_static_works=yes lt_cv_prog_compiler_static_works_CXX=yes lt_cv_prog_compiler_static_works_F77=yes lt_cv_prog_gnu_ld=yes lt_cv_prog_gnu_ldcxx=yes lt_cv_sharedlib_from_linklib_cmd='printf %s\n' lt_cv_shlibpath_overrides_runpath=yes lt_cv_sys_global_symbol_pipe='sed -n -e '\''s/^.*[ ]\([ABCDGIRSTW][ABCDGIRSTW]*\)[ ][ ]*\([_A-Za-z][_A-Za-z0-9]*\)$/\1 \2 \2/p'\'' | sed '\''/ __gnu_lto/d'\''' lt_cv_sys_global_symbol_to_c_name_address='sed -n -e '\''s/^: \(.*\) .*$/ {"\1", (void *) 0},/p'\'' -e '\''s/^[ABCDGIRSTW][ABCDGIRSTW]* .* \(.*\)$/ {"\1", (void *) \&\1},/p'\''' lt_cv_sys_global_symbol_to_c_name_address_lib_prefix='sed -n -e '\''s/^: \(.*\) .*$/ {"\1", (void *) 0},/p'\'' -e '\''s/^[ABCDGIRSTW][ABCDGIRSTW]* .* \(lib.*\)$/ {"\1", (void *) \&\1},/p'\'' -e '\''s/^[ABCDGIRSTW][ABCDGIRSTW]* .* \(.*\)$/ {"lib\1", (void *) \&\1},/p'\''' lt_cv_sys_global_symbol_to_cdecl='sed -n -e '\''s/^T .* \(.*\)$/extern int \1();/p'\'' -e '\''s/^[ABCDGIRSTW][ABCDGIRSTW]* .* \(.*\)$/extern char \1;/p'\''' lt_cv_sys_global_symbol_to_import= lt_cv_sys_max_cmd_len=1572864 lt_cv_to_host_file_cmd=func_convert_file_noop lt_cv_to_tool_file_cmd=func_convert_file_noop lt_cv_truncate_bin='/bin/dd bs=4096 count=1' pkg_cv_DEPS_CFLAGS=-I/usr/local/include pkg_cv_DEPS_LIBS='-L/usr/local/lib -lfftw3 -lgeners -lkstest' ## ----------------- ## ## Output variables. ## ## ----------------- ## ACLOCAL='${SHELL} '\''/home/igv/Hepforge/npstat/trunk/missing'\'' aclocal-1.16' AMDEPBACKSLASH='\' AMDEP_FALSE='#' AMDEP_TRUE='' AMTAR='$${TAR-tar}' AM_BACKSLASH='\' AM_DEFAULT_V='$(AM_DEFAULT_VERBOSITY)' AM_DEFAULT_VERBOSITY='1' AM_V='$(V)' AR='ar' AUTOCONF='${SHELL} '\''/home/igv/Hepforge/npstat/trunk/missing'\'' autoconf' AUTOHEADER='${SHELL} '\''/home/igv/Hepforge/npstat/trunk/missing'\'' autoheader' AUTOMAKE='${SHELL} '\''/home/igv/Hepforge/npstat/trunk/missing'\'' automake-1.16' AWK='mawk' CC='gcc' CCDEPMODE='depmode=gcc3' CFLAGS='-g -O2' CPPFLAGS='' CSCOPE='cscope' CTAGS='ctags' CXX='g++' CXXCPP='g++ -E' CXXDEPMODE='depmode=gcc3' CXXFLAGS='-std=c++11 -O3 -Wall -W -Werror' CYGPATH_W='echo' DEFS='-DPACKAGE_NAME=\"npstat\" -DPACKAGE_TARNAME=\"npstat\" -DPACKAGE_VERSION=\"5.9.0\" -DPACKAGE_STRING=\"npstat\ 5.9.0\" -DPACKAGE_BUGREPORT=\"\" -DPACKAGE_URL=\"\" -DPACKAGE=\"npstat\" -DVERSION=\"5.9.0\" -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_STRINGS_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_UNISTD_H=1 -DSTDC_HEADERS=1 -DHAVE_DLFCN_H=1 -DLT_OBJDIR=\".libs/\"' DEPDIR='.deps' DEPS_CFLAGS='-I/usr/local/include' DEPS_LIBS='-L/usr/local/lib -lfftw3 -lgeners -lkstest' DLLTOOL='false' DSYMUTIL='' DUMPBIN='' ECHO_C='' ECHO_N='-n' ECHO_T='' EGREP='/bin/grep -E' ETAGS='etags' EXEEXT='' F77='g77' FFLAGS='-g -O2' FGREP='/bin/grep -F' FLIBS=' -L/usr/lib/gcc/x86_64-linux-gnu/11 -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/11/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/11/../../.. -lgfortran -lm -lquadmath' GREP='/bin/grep' INSTALL_DATA='${INSTALL} -m 644' INSTALL_PROGRAM='${INSTALL}' INSTALL_SCRIPT='${INSTALL}' INSTALL_STRIP_PROGRAM='$(install_sh) -c -s' LD='/bin/ld -m elf_x86_64' LDFLAGS='' LIBOBJS='' LIBS='' LIBTOOL='$(SHELL) $(top_builddir)/libtool' LIPO='' LN_S='ln -s' LTLIBOBJS='' LT_SYS_LIBRARY_PATH='' MAKEINFO='${SHELL} '\''/home/igv/Hepforge/npstat/trunk/missing'\'' makeinfo' MANIFEST_TOOL=':' MKDIR_P='/bin/mkdir -p' NM='/bin/nm -B' NMEDIT='' OBJDUMP='objdump' OBJEXT='o' OTOOL64='' OTOOL='' PACKAGE='npstat' PACKAGE_BUGREPORT='' PACKAGE_NAME='npstat' PACKAGE_STRING='npstat 5.9.0' PACKAGE_TARNAME='npstat' PACKAGE_URL='' PACKAGE_VERSION='5.9.0' PATH_SEPARATOR=':' PKG_CONFIG='/bin/pkg-config' PKG_CONFIG_LIBDIR='' PKG_CONFIG_PATH='/usr/local/lib/pkgconfig' RANLIB='ranlib' SED='/bin/sed' SET_MAKE='' SHELL='/bin/bash' STRIP='strip' VERSION='5.9.0' ac_ct_AR='ar' ac_ct_CC='gcc' ac_ct_CXX='g++' ac_ct_DUMPBIN='' ac_ct_F77='g77' am__EXEEXT_FALSE='' am__EXEEXT_TRUE='#' am__fastdepCC_FALSE='#' am__fastdepCC_TRUE='' am__fastdepCXX_FALSE='#' am__fastdepCXX_TRUE='' am__include='include' am__isrc='' am__leading_dot='.' am__nodep='_no' am__quote='' am__tar='$${TAR-tar} chof - "$$tardir"' am__untar='$${TAR-tar} xf -' bindir='${exec_prefix}/bin' build='x86_64-pc-linux-gnu' build_alias='' build_cpu='x86_64' build_os='linux-gnu' build_vendor='pc' datadir='${datarootdir}' datarootdir='${prefix}/share' docdir='${datarootdir}/doc/${PACKAGE_TARNAME}' dvidir='${docdir}' exec_prefix='${prefix}' host='x86_64-pc-linux-gnu' host_alias='' host_cpu='x86_64' host_os='linux-gnu' host_vendor='pc' htmldir='${docdir}' includedir='${prefix}/include' infodir='${datarootdir}/info' install_sh='${SHELL} /home/igv/Hepforge/npstat/trunk/install-sh' libdir='${exec_prefix}/lib' libexecdir='${exec_prefix}/libexec' localedir='${datarootdir}/locale' localstatedir='${prefix}/var' mandir='${datarootdir}/man' mkdir_p='$(MKDIR_P)' oldincludedir='/usr/include' pdfdir='${docdir}' prefix='/usr/local' program_transform_name='s,x,x,' psdir='${docdir}' runstatedir='${localstatedir}/run' sbindir='${exec_prefix}/sbin' sharedstatedir='${prefix}/com' sysconfdir='${prefix}/etc' target_alias='' ## ----------- ## ## confdefs.h. ## ## ----------- ## /* confdefs.h */ #define PACKAGE_NAME "npstat" #define PACKAGE_TARNAME "npstat" #define PACKAGE_VERSION "5.9.0" #define PACKAGE_STRING "npstat 5.9.0" #define PACKAGE_BUGREPORT "" #define PACKAGE_URL "" #define PACKAGE "npstat" #define VERSION "5.9.0" #define HAVE_STDIO_H 1 #define HAVE_STDLIB_H 1 #define HAVE_STRING_H 1 #define HAVE_INTTYPES_H 1 #define HAVE_STDINT_H 1 #define HAVE_STRINGS_H 1 #define HAVE_SYS_STAT_H 1 #define HAVE_SYS_TYPES_H 1 #define HAVE_UNISTD_H 1 #define STDC_HEADERS 1 #define HAVE_DLFCN_H 1 #define LT_OBJDIR ".libs/" configure: exit 0